What’s New in Test Automation?

With the arrival of Agile and DevOps development technologies, the software development industry has gone through a significant disruption. Which naturally, has impacted test automation as well. Quality Assurance professionals have had to quickly adapt to the changes in the industry to stay relevant.In some ways, the pace of change is only accelerating. Let’s take a look at some of the latest trends in test automation:
Test Automation Latest Trends
 

  1. Enhanced Scope of Test Automation:
  2. Test automation was primarily designed to test the application against its expected behavior. However, today, automation teams have to think past the actual scope of test validations to verify a build before its release. Test automation is now used in CI/CD modeling, continuous integration, and delivery, aggressively. 

    With the advent of CI-CD and agile development, delivery models with faster time-to-market are coming into vogue. The coverage of test automation has spread across Mobile and Web applications, enterprise systems, and even IoT applications. All automation tools now support a wide variety of application streams.

  3. Increased Pressure to Shorten Delivery Cycles:
  4. The need for test management tools has expanded to facilitate ever-shortening delivery cycles. Companies are investing heavily in improving their development and delivery processes by making use of new and improved tools. Test automation is an integral part of this process.

    Frequent changes in technologies, platforms, and devices have put tremendous pressure on software development teams to deliver solutions faster and more often. By integrating test automation with development, companies can stay on track with market requirements and shorten their delivery cycles.

  5. Integration:
  6. As mentioned earlier, integration plays a pivotal role in shortening delivery cycles. It is also vital when it comes to facilitating test automation intelligently. For smart testing and analytics, the data is consolidated from diverse sources such as requirement management systems, change control systems, task management systems, and test environment.

    The expectation in today’s software development scenario is that the automation suite can execute untended on each code drop regardless of the environment. The need is for it to run through and log failures and successes. In other words, the scope of automation has evolved from test validation to a fully unattended build certification.  Though the code required to verify a scenario is the same, software teams have to evaluate all the ways to integrate it to perform unattended integrations.

  7. Big Data Testing:
  8. Today we live in the day and age of big data. Businesses are going through digital transformation, and data holds critical importance in gaining insights. Essentially, Big Data is large volumes of multiple different kinds of data that is generated at a tremendous velocity. Naturally, this change brings about the need for Big Data testing.

    Test automation in Big Data testing focuses on both performance testing and functional testing. In Big Data testing, it is vital to verify that terabytes of data are favorably processed using commodity cluster and other supportive components. The success of Big Data testing largely depends on the quality of the data.  Hence, the quality of data is validated before test automation begins.

    The data quality is reviewed based on several characteristics such as conformity, accuracy, validity, consistency, duplication, data completeness, etc.

  9. Union of Test Automation and Machine Learning:
  10. Machine learning has brought about some significant changes in workflows and processes. This includes the test automation processes too. In test automation, machine learning can be used to classify redundant and unique test cases; to predict the critical parameters of software testing processes based on historical data; to determine the tests cases which need to be executed automatically; to extract keywords to achieve test coverage; to identify high-risk areas of the application for the prioritization of regression test cases.

Conclusion:

As technology gets more advanced, there is tremendous pressure for development iterations to get shorter. By default, this makes quality-related expectations more complex. With massive shifts in the software development field, the test automation process has evolved tremendously, and it will continue to develop in the future.

In a race against time and driven by the need for world-class quality, test automation will remain a strategic investment for businesses to reduce costs while overcoming challenges related to quality and time-to-market. On that journey, of course, only one thing can be predicted with any degree of certainty. And it’s that as software development keeps evolving, testing and test automation will keep evolving as well.

What is Rapid Application Development and why you should care?

Even before the Agile methodology became commonplace in software development, the Rapid Application Development model brought flexibility to the entire development process. Rapid Application Development quickly swept the Waterfall model out of its place. And it continues to deliver value.

Rapid Application Development helps to quickly develop prototypes to test functions and features without having to worry too much about their impact on the end product. With RAD, you can add or remove functionalities, change the design of a software product and clean it up by eliminating extra fluff – all without harming the end product.

Rapid Application Development

Markets and Markets have predicted the Rapid Application Development market will grow from USD 7.8 bn in 2018 to USD 46.2 bn by 2023. This growth is driven by the ever-rising demand for faster software programming with low-code, and customizable and scalable solutions.

But how did the RAD model come to light? Is it the right model for your software development process? When should you choose to work with Rapid Application Development?

 

Let’s find out.

Rapid Application Development in a Nutshell

The Rapid Application Development model prioritizes quick prototyping instead of long, drawn-out development and testing cycles. With Rapid Application Development, developers can make multiple iterations to their software without having to start from scratch each time.

Rapid Application Development has been around since the 1980s. So it’s not new but has always found value in projects where the continuous evaluation of development is at the core.

Steps Involved in RAD

Although RAD has massively evolved over the years, these four basic steps remain at its heart.

  • Define the Requirements– The Rapid Application Development model does not need you to start with a detailed list of specifications. Instead, it encourages developers to ask for requirements at the time. As the project advances deeper into the development cycle, the specifics of the requirement can be further ironed out. This step sets the stage for the ultimate success of the project. This is where the developers, users, stakeholders, and other members sit down to discuss the goals and expectations of the project. They also address the current and potential challenges that may hinder their path during development. The key to successful RAD is for all the stakeholders and users to be involved right from the beginning to define the goals and expectations.
  • Build the Prototype – Once the scope of the project is clearly chalked out, it’s time for the developers to dive in. In this step, the basic user design is built through prototype iterations. During this phase, users or clients collaborate with developers to make sure their requirements are met at every step in the design process. The developer designs a prototype, the user tests it and provides feedback on how it can be improved. Ultimately, through this collaboration, there is less chance that something will slip through the cracks.
  • Rapid Construction – The prototypes and beta systems developed in the previous stage are now converted into working models. Since a large number of glitches and gaps would have been addressed during the prototyping process, the developers can now move at full speed in constructing the product. It is fascinating that even during this phase, the end users provide inputs, and suggest modifications which are subsequently accommodated.

The last two steps repeat iteratively until the client’s objectives and expectations are met.

  • Finalizing and deployment – In this step, the features, user experience, and interface of the software are finalized with the product owner or client. Before delivering or deploying the product at the client side, developers test the software for stability usability and maintainability.

RAD- When And Why You Stand to Gain

Does it make sense to use the Rapid Application Development model for each type of software development? Perhaps not. Here’s when you can use Rapid Application Development-

When you have access to a pool of users

If you have got users who can give consistent and reliable feedback on your prototypes, the Rapid Application Development is a great model. Since the RAD model has its roots in the feedback developers receive throughout the cycle, is not the best model without access to dependable sources who will use and review your product in an unbiased manner.

When you Need Quick Delivery:

If you are on a tight deadline, RAD could help. You can cut down on the requirement planning and design phases which individually consume time, and begin prototyping rapidly to deliver something that works sooner. Rapid Application Development is an on-the-fly approach that leads to quick development. Some estimates are that the RAD approach can slash the traditional technology lifecycle by 80 percent.

Rapid Application Development-

  • Is a flexible model that can adapt to changes.
  • Minimizes the overall project risk.
  • Is easy to transfer deliverables in the form of high-level abstractions scripts and intermediate codes.
  • Allows Iterative prototyping that reduces the possibility of bugs and defects.

Clearly, with RAD, an organization can build a software product faster, eliminating the need for long development lifecycles and hassle-filled development journeys. Does that sound like something you would consider for your next software development project?

Mistakes to Avoid While Building a MVP

We all know the history of the MVP. The concept of MVP, an offshoot of the Lean Startups movement, was popularized by Eric Ries-a consultant and writer on startups. The minimum viable product (MVP) is a development method in which a new product is developed with just enough features to satisfy the initial adopters. The final, comprehensive set of features is only planned and developed after analyzing feedback from the product’s early users. This feedback is gathered often, and product versions are released often.

In its essence, a minimum viable product is the most trimmed down version of a product that is still suitable for release. In simple words, the MVP has sufficient value for people to adopt or buy it initially. A key characteristic is that it displays promising future benefits to retain initial adopters. A vital component of the plan is that it renders a feedback loop to support future development.

An obvious drawback or rather difficulty with this development methodology is that it assumes that the initial adopters can visualize the outlook of the final product and give the required feedback to help developers progress.

Mistakes to Avoid While Building a MVP

Have you wondered, why the concept of an MVP was introduced in the first place? Well, because in a startup environment, mistakes are the norm. In fact, studies show that nine out of ten startups fail. To get around this failure rate, many startups began to build a Minimum Viable Product. The aim was to embrace the fact that they would fail but to fail fast and fail often so that they still had resources to recover.

However, Several Startups are facing failure despite going the MVP route. Why?

This happens because when companies expose an MVP to a test audience, they often get distracted. They lose sight of the end objective. Sometimes, the immediacy of the task of making the next release successful draws them away from the larger objective of pursuing a more significant market segment. The big picture may call for them to change direction or to design an unusual feature. This could lead to cost-intensive rework, loss of focus, and, in turn, a failed product.

To Avoid Failure and Make the Most of Your Minimum Viable Product, Here are a Few Mistakes to Avoid While Building an MVP.

  1. Targeting the Masses:
  2. You cannot begin your development with the mindset that your product is something that everyone needs. You must design your product in a way that it focuses on a narrow market segment that will benefit the most from using your product immediately. If you try to create a one-size-fits-all type of product, you will not be able to do anything competently.
     
    Facebook is a perfect example of an organization avoiding this trap. While yes, today, Facebook is a platform that everyone uses; they didn’t start this way. Their initial focus was on college students. By customizing their product for a narrow segment, Facebook managed to build a better and more focused platform. This enabled them to expand later.

  3. Overtaxing the Initial Users:
  4. While seeking feedback, too often, companies make the mistake of asking too many questions and over-taxing the users. However, on the flipside, even a deficit of information from the users is a drawback. Companies are often stuck in the limbo of asking too few questions to ensure maximum participation but end up getting the feedback they can’t use because it is not specific or actionable enough. Therefore, it is essential to make the testing process as user-friendly as possible and create user surveys that ask detailed yet relevant questions.

  5. Choosing the Wrong Features:
  6. The primary purpose of an MVP is to test the idea before proceeding to complete development. An ideal MVP has only a few core functionalities that offer the end-users an understanding of the product and the issues it resolves. The selection of features is a crucial process in the development of an MVP. Too many companies spend a significant amount of time and budget in polishing the design and adding more and more features to impress the audience.
     
    The downside of this is that after spending the most vital part of a project budget on building an over-designed MVP the product idea may be rejected by the audience. On the other end of the spectrum, some companies adopt the ultra-minimalist approach and ignore the viability aspect of the product. Since their focus is on reducing a set of features, they overlook the elements that make their product unique and useful for users. The product fails to find favor with the users because it will not deliver sufficient meaningful value.

 
Lastly, it is important to keep in mind that MVPs tend to get enormous amounts of data and ideas from customers.  Much of that data may suggest changes that may not be suitable. While yes, these ideas and suggestions are great to broaden your horizon, it may be wiser to avoid making significant adjustments immediately. If you expand too quickly you may not be able to gauge which features are creating a positive impact and which are stirring a negative response.
 
Do remember, there will be plenty of time to progress into new directions once you are confident that your product is well-designed and that it meets the needs of your core clientele.

Agile, DevOps or Others – Which software development methodology is right for you?

The uber-significance of software in the business world has given rise to an extraordinary windfall of software development methodologies. It’s also true that the software methodology you choose can make or break your business. The wrong choice can result in miscommunication, schedule and cost slippage, wasted time, and resource burnout. So, how to choose?

software development : agile or devops

Factors to Consider while Choosing the Right Software Development Methodology

Choosing the right software development methodology among many alternatives could be challenging. Yet, this choice is crucial for it will decide the fate of your project. For teams having a hard time choosing, here are the top factors to consider:

  • Size of project: The size of the project is one of the first aspects to keep in mind while choosing a development methodology. While small projects with limited requirements will only need a handful of resources for successful delivery, large projects that span software generations would require many architects, developers, and testers spread across locations.The size and scale of the project determine the project management plan and the number of developers needed to handle it. You must choose a methodology that allows for constant collaboration and feedback between the teams so the end product meets the intended requirements, and is delivered within time and budget.
  • Fluctuations in requirement: With customer demands and market dynamics constantly changing, changes must be made to the software to drive better experiences. Dynamic requirements also determine the methodology you choose.
    If stability and predictability are the norm and ongoing changes minimal, traditional methodologies work well. However, if you need to constantly incorporate new features and enhancements in your code, you need to choose a more modern methodology that allows for such changes to be embedded – without impacting cost or schedule.
  • Cost of delays: Staying on schedule is important for any software development project. Yet, with requirements constantly changing, software projects invariably overshoot deadlines. Obviously, this substantially impacts costs too.
    The larger the scale the more the pressure to deliver on time. If not, the project risks massive cost of delays. For smaller projects,the impact of a schedule overrun is not so drastic. Make sure to choose a methodology factoring in how delays impact the project and your organization.
  • Team locations: The software methodology you choose must also depend on how dispersed your team is.
    If your developers, engineers, and testers are located across different geographies, miscommunication, confusion, and missteps may become more common in the development process. That suggests a greater need for coordination, coherence, and accountability. A transparent and collaborative project management regimen is needed. Then teams can stay updated about the progress of the project, and work together towards common goals.
  • Feedback frequency: In a customer-driven world, there is a constant clamour for new features. Some projects require customer feedback to be constantly incorporated. While some projects may require more such feedback elements to be baked in due to the complexity and the size of the project, some require far less. The software development methodology you choose will depend on how frequently you have to incorporate customer feedback.

The Options at Hand:

A variety of factors come together to have a considerable bearing on the project. No matter the type of software you are building –choosing the right software development methodology is critical to ensure happy teams, on-time delivery, and easily-met business and end-user needs.

Here are the options you have at hand:

  • Even today, if your requirements are clear and fixed, the best-suited development methodology is Waterfall. Offering a well-defined phased approach to software development, the waterfall methodology allows you to design the solution as visualized. Since there will be no surprises or changes in the requirements along the course of development, you can seamlessly proceed with development, integration, and testing based on the design, and deliver the product on time.
  • If your requirements are fairly straightforward, but you do not have a clear picture of how the solution should be built, you will need to choose a methodology that enables constant feedback. Kanban allows you to constantly monitor the product, check on tasks in progress, prioritize them if needed, and receive feedback on a continuous basis. Such feedback ensures the software you are developing is in line with the intended need and as visualized at the beginning.
  • If your requirements change frequently, the Agile methodology will allow you to meet your goals better than others. Using Agile, you can build projects in sprints and accommodate a fair number of changes and enhancements – with less cost of delay. Since each sprint needs to be completed in a given time frame, you can ensure you stick to the commitment and complete all tasks within the sprint – in time and within budget.
  • If your requirements change frequently, your teams are dispersed across the globe, and the cost of delay is high, DevOps can help you drive rapid software delivery. This is especially beneficial in case of products that live on the Cloud -like SaaS products. By fueling better communication and collaboration between the development and operations teams, DevOps helps to condense development cycles, increase deployment frequency, and meet software needs quickly and efficiently. It helps reduce development complexity, detect and resolve issues faster, and allows you to deliver high-quality, innovative software.

Always look at the Bigger Picture:

It is crucial to make the right decisions about tools, resources, schedule, and budget in software development. Do consider aspects such as the size of the project, fluctuations in the requirement, cost of delays, team location as well as the feedback frequency and then make the choice of development approach.

The right software development methodology will allow you to deliver quality outcomes in the time and budget allotted. When looking for the best software development methodology, ensure you consider the bigger picture. This choice will give you the best results for the effort, money, and time invested. How did you zero in on your product development approach?

Talk to our Expert Regarding Software Methodlogy

What you must consider for your Enterprise AI strategy?

A chatbot is the most in-your-face use case of AI, but it’s easy to underestimate the opportunities that AI can help us realize. By some estimates, by 2023 around 40% of all internal operations teams in Enterprises will be AI-enabled. The flip side is that even though the growth opportunities are huge, it will take time, effort, and a concerted strategy to realize the true potential.

Enterprise AI Strategy
Let us look at the key considerations to factor in while embarking on the AI journey.

  1. Definite Use Cases:

    It is imperative to have a definite use case in mind before one thinks of implementing AI in your Enterprise. Many implementations fail simply because they are implemented with no thought about the end goal to be achieved. To avail a great ROI, it is extremely important that one has a clear definition of the specific business goals to shoot for. For instance, a customer service operation may want to reduce the number of customer service calls by a factor of 50%. Chatbot-enabled engines could help -and after a defined period you can establish clearly if the initiative was a success.

  2. Think Big Start Small:

    It is best to have lofty goals while aiming for a transformation with AI but start with a small test or a pilot project. It’s always prudent to test the waters before taking the plunge. Chose one particular LOB, or a small department to test AI and its viability for this particular endeavor. This will throw up the problems one can encounter while undergoing a transformation. And at the same time, you will also identify the challenges resident within the ecosystem that may have to be addressed for achieving a seamless transformation.

  3. Creation of a Knowledge Repository:

    The success of an AI implementation is dependent on how robust the underlying knowledge base is. This requires data, lots of it. The AI will learn as it goes along -but even at the stage of training the AI, vast amounts of data is needed. The idea is to have the AI system define how a problem can be solved and be driven by the relevant insights the AI provides. By having a highly mature algorithm driven by a robust database you can improve the quality of the insights available. The primary difference between a normal knowledge repository and a Knowledge repository for AI is in the structure and the content. For AI, an interface along with highly structured data which can be queried is necessary.

  4. Build or Buy and choosing the Correct Partner:

    AI may be necessary for every organization but not every organization will have the requisite resources to implement it on their own. You could build the expertise, or you may have to work with a partner.Picking the right partner is a crucial decision. The selection should be driven by considerations like the availability of skilled human resources, successful past implementation,understanding of your business challenges, and their future roadmap.

  5. Data Quality:

    For AI data quantity is not enough, data quality is paramount. AI is driven by Data Science and statistical algorithms. These algorithms become trustworthy if the data quality of the data set on which the system is being trained and implemented is pure and pristine. That is the reason why there should be a state-of-the-art data quality monitoring system. You may have to fix the data duplication issues and weed out the corrupt and broken data.

  6. Cloud or On-Premise:

    Once put into place, the knowledge repository will increase in size at an exponential rate. A tsunami of streaming data will fill up the data storage really fast. Hence many organizations consider the cloud for storing the data. The answer to the question of whether to go for cloud or stay on-premise will be driven by factors like the security and compliance requirements, apart from the cost and storage volume needed.

  7. Right Resource Pool:

    Irrespective of the decision to build or buy it’s true that there are not many trained and experienced human resources out there. It is common to underestimate the demands AI will make on the business. This is not just about the technical resources needed to implement the systems. AI strategies sometimes fall apart because the Enterprise didn’t train or develop their functional resources to cater to the new ways of working. Business processes will change, agility will increase, and responsibilities will shift -your people will have to be ready.

  8. Top Management Buy-in:

    Like any other strategic initiative, the involvement of the top management is a key factor for the success of any AI implementation. Many Enterprises still work top-down. With top management throwing its weight behind a project, the probability of its success increases exponentially. The organization starts treating the implementation with the required seriousness. Resources get allocated, Results get tracked.

Conclusion:

As you can see, there are quite a few factors to bake into the implementation of your Enterprise AI initiative. Knowing these factors and staying hyper-focused will help you stay on track with your AI initiative. And implementing a robust AI strategy that has the greatest chance of delivering business impact is what it’s all about -isn’t it?

Test Automation for Microservices- Here’s What You Need to Know

We have written a couple of times in the past about Microservices. The approaches are evolving, and this blog is an attempt to address a specific question -while testing microservices, does test automation have a role?

Just a little refresher first. As the name suggests, microservices are nothing but a combination of multiple small services that make up a whole. It is a unique method of developing software systems that focus on creating single-function modules with well-defined interfaces and operations. An application built as microservices can be broken down into multiple component services. Each of these services can be deployed, modified, and then redeployed individually without compromising the integrity of an application. This enables you to change one or more distinct services (as and when required) instead of having to redeploy the application as a whole.

Microservices are also highly intelligent. They receive requests, process them, and produce a response accordingly. They have smart points that process information and apply logic, and then direct the flow of the information.

automation testing microservices

Microservices architecture is ideal in the case of evolutionary systems, for eg. where it is not possible to thoroughly anticipate the types of devices that may be accessing the application in the future. Many software products start based on a monolithic architecture but can be gradually revamped to microservices as and when unforeseen requirements surface that interact over an older unified architecture through APIs.

Why is Testing for Microservices Complicated?

In the traditional approach to testing, every bit of code needs to be tested individually using unit tests. As parts are consolidated together, they should be tested with integration testing. Once all these tests pass, a release candidate is created. This, in turn, is put through system testing, regression testing, and user-acceptance testing. If all is well, QA will sign-off, and the release will roll out. This might be accelerated while developing in Agile, but the underlying principle would hold.

This approach does not work for testing microservices. This is mainly because apps built on microservices use multiple services. All these services may not be available on staging at the same time or in the same form as they are during production. Secondly, microservices scale up and share the demand. Therefore, testing microservices using traditional approaches can be difficult. In that scenario, an effective way to conduct microservices testing is to leverage test automation.

Quick Tips on How to Automate Testing for Microservices:

Here are some quick tips that will help you while testing your microservices-based application using test automation.

  • Manage each service as a software module.
  • List the essential links in your architecture and test them
  • Do not attempt to gather the entire microservices environment in a small test setup.
  • Test across different setups.

How to Conduct Test Automation for Microservices?

  1. Each Service Should Be Tested Individually: Test automation can be a powerful mechanism for testing microservices. It is relatively easy to create a simple test script that regularly calls the service and matches a known set of inputs against a proposed output. This function by itself will free up your testing team’s time and allow them to concentrate on testing that is more complex.
  2. Test the Different Functionalities of your Microservices-based Application: Once the vital functional elements of the microservices-based application have been identified, they should be tested much like you would conduct integration testing in the traditional approach. In this case, the benefits of test automation are obvious. You can quickly generate test scripts that are run each time one of the microservices is updated. By analyzing and comparing the outputs of the new code with the previous one, you can establish if anything has changed or has broken.
  3. Refrain from Testing in a Small Setup: Instead of conducted testing in small local environments, consider leveraging cloud-based testing. This allows you to dynamically allocate resources as your tests need them and freeing them up when your tests have completed.
  4. Test Across Diverse Setups: While testing microservices, use multiple environments to test your code. The reason behind this is to expose your code to even slight variations in parameters like underlying hardware, library versions, etc. that might affect it when you deploy to production.

Microservices architecture is a powerful idea that offers several benefits for designing and implementing enterprise applications. This is why it is being adopted by several leading software development organizations. A few examples of inspirational software teams leveraging microservices include Netflix, Amazon, eBay, etc. If like these software teams, your product development is also adopting microservices then testing would undoubtedly be in focus. As we have seen, testing these applications is a complex task and traditional methods will not do the job. To thoroughly test an application built on this model, it may be essential to adopt test automation. Would you agree?

How AI can transform Enterprises?

Artificial Intelligence, more popularly known as AI, might no longer be the new technology on the block, but it is ‘the’ technology that everyone is talking about. Facial recognition, digital assistants, autopilots etc. are examples of the existing AI around us. AI is emerging as that disruptive technology that will change the way we live and work. While AI has been seen often in a consumer-centric world, the enterprise too is warming up to this technology.

2018 witnessed widespread adoption of AI in different industries as organizations realized the value AI brought to the table – be it in improving operations, assisting the data analytics drive, boosting innovation, and improving customer experience amongst other things. Owing to the immense value AI brings to the table, the global AI market size is expected to reach $169,411.8 million in 2025, from $4,065 million in 2016 growing at a CAGR of 55.6% from 2018 to 2025 according to MarketWatch.

So, what transformative value does AI bring for the enterprise? Here’s a look at how AI will transform enterprises and change the future of work.

AI tranform enterprises

                                                     

  1. The New age of Automation: AI is going to give automation the boost that it needs. As enterprises look towards technologies such as Robotic Process Automation (RPA), with AI we shall be moving into the world of Intelligent Process Automation. IPA combines process automation with Robotic Process Automation (RPA) and Machine learning (ML) and creates choreographic connections between people, processes, and systems. IPA will not only automate structured tasks but also generate intelligence from process execution.

    IPA is all set to increase the level of transparency in business processes, optimizing back-office operations, increasing process efficiency and customer experience, and improving workforce productivity considerably. Along with this, IPA also holds the promise of reducing costs and risks and promises more effective fraud detection. Owing to these benefits, the IPA market is expected to be worth $13.75 billion by 2023.

  2. The Rise and Rise of Chatbots: The friendly chatbot has already made some inroads into the enterprise. With AI, the chatbot invasion is going to become more pervasive in the enterprise of the future. Customer-facing industries such as retail, healthcare, banking, and financial services shall witness the rise of AI-powered voice assistants such as Alexa or Siri to create interactive experiences for the customer without pushing the load of delivering exceptional customer experiences on the staff alone.

    Chatbots will also become the norm to service the internal customers of the organizations, the employees. Enterprise chatbots will be powered by AI technologies such as NLP (Natural Language Processing), semantic search, and voice recognition. They will enhance search capabilities and deliver a new way for employees to interact with corporate data to improve their productivity.

  3. AI and the UX Impact: The focus on User Experience or UX is only going to keep increasing. With AI, the user experience will not be driven by guesswork but by faster analysis of the right data, by the enterprises in the future.  User experiences with software products, even within the enterprise, have to mimic consumer-grade experiences.

    Fluid, intuitive, efficient, and highly-personalized user experiences are going to be the norm. UX is also going to be the defining factor in product success and acceptance. Enterprises will look at the insights provided by AI by intelligent information gathering and identifying patterns to deliver greater value to the end-user. This will make the user experience of products highly intuitive and intelligent as well.

  4. Greater Intelligent Customization Capabilities: As we move deeper into the age of personalization, enterprises will have to look towards technologies such as AI to develop intelligent customization capabilities. Data is already improving the customization capabilities of enterprises.

    With cognitive technologies such as AI, they will be able to further improve their customization capabilities and create products that individual users will love. Leveraging user data and faster data-processing capabilities, AI can speed up interactions and provide intelligent insights to develop products and solutions that can be highly customized to meet user demands.

  5. Cutting Edge Analysis To Bolster Data-Driven DecisionsAI will be leveraged in the enterprise to perform advanced data investigation in less time to improve business process, product, and service efficiencies. AI technologies have the capability to analyze usage patterns and then deliver deep insights that will take data-driven decision making to the next level.

    Whether it is for predictive maintenance or predictive analytics for product development, or risk management or planning, the AI impact will make the enterprise smarter and more proactive in its decision-making.

  6. AI In Software Development and TestingSoftware Development and Testing will also feel the AI impact as this technology gets more pervasive. To respond to the market need for robust, reliable, and high-quality software that is delivered faster, AI technologies will get ingrained into the development and testing lifecycle.

    With self-learning algorithms that are designed to self-improve, enterprises will be looking at improving the efficiency of the process of software development. They will leverage automated code-generation, among other things, and achieve a shorter time to market with greater confidence.

While AI has met with a certain resistance in the past, the coming years will see this technology achieve greater maturity. Given the immense value that AI can deliver, it is only a matter of time before AI will become a necessity for the enterprises that wish to remain relevant in this ever-evolving and competitive marketplace.

Trends Impacting Software Development in 2019

Technology has created our software-defined world of today. As technologies change and evolve, we see the rise of new software development trends to further augment this growth. 2018 was no exception. We saw some exciting developments in the world of software development. We witnessed the rise of cloud-based software development, the cementing of DevOps, and the ever-growing importance of testing. But what about the year ahead? Here’s a look at some of the trends that will impact software development in 2019.

Trends impacting software development 2019

  1. Artificial Intelligence (AI):

    Gartner estimates that the revenue from the AI industry will touch $1.2 trillion by the end of 2019. By 2022 the business value derived from AI is expected to reach $3.9 trillion. With the digital transformation wave taking over almost all organizations, it is clear that AI will continue to trend in the software universe for the next couple of years.

    In 2019 we will see the use of AI to speed up and improve the accuracy of software development- be it in automatic debugging, for creating intelligent assistants to speed up development processes, to automate code generation, or to create and train an automated system to produce accurate estimates to develop MVPs faster…the AI impact will be all around and hard to ignore.

  2. Blockchain:

    Blockchain, the meta-technology, holds the promise of completely reshaping software development. Blockchain consists of a single ledger of transactions and enables smart contracts. It has a distributed database that is accessible to a peer-to-peer network but is protected against unauthorized access. The technology is secured by cryptographic technique making the applications developed using this tech more secure.

    Blockchain has already made its presence felt in several different sectors be it retail, banking, financial services, healthcare, and public administration. It is only a matter of time before Blockchain becomes a prime focus for organizations involved in software development. As security becomes top of the mind, the need for blockchain-based applications will increase. 2019 looks like a good year to jump on this technology trend.

  3. Progressive Web Apps:

    Progressive Web Apps leaped to our attention when Gartner announced it as a software trend in 2017. In 2019 however, with a more mature app ecosystem in place, we expect to see Progressive Web apps become more dominant and gain that promised place. As the app economy gets stronger and the mobile environment evolves, progressive web apps are gradually going to become the new normal in this changing environment.
    Research shows that progressive web apps show a 68% increase in mobile traffic and are 15 times faster to load and install as compared to native apps. Progressive web apps also require 25 times less device storage space in comparison to native apps. These applications are also less complex to develop, are easy to maintain and provide the benefits of mobile experience with the features of browser technology. What’s not to love?

  4. Security Rules:

    Security has been on everyone’s mind in the software development space. The increased focus of security during software development is only going to increase in this year. Research from Alert Logic showed that data loss and leakage is one of the biggest concerns for cybersecurity professionals (67%). Threats to data privacy were the concern for 61% while 53% were concerned with breach of confidentiality.

    Owing to the huge impact security issues can have on a software product and its users, organizations are conscious of baking security into the process of software development. Software development companies also have to keep a close eye on regulatory considerations for specific industries.

    They must also follow security best practices and ensure that all security guidelines and protocols are met consistently. To make security more robust, organizations are also looking at Managed Security Providers or MSP’s for robust application security without compromising on development timelines.

  5. Automated Testing:

    While test automation has been around for a while, automated testing will continue to be a trend in 2019. This will continue for as long as there is a need to release better-tested products into the market faster -that means, forever! As testing gets deeply ingrained into every software development methodology, test automation will get even more pervasive with testing teams striving for greater levels of automated test coverage.

    In 2019 we will witness test automation leveraging AI for better test accuracy. Tests will become more comprehensive, more intelligent, and more dependable. Even with that, that they will become faster and less taxing. The products will become better tested and more robust as a result.

    Borrowing from, and evolving the technologies that help to automate testing, Robotic Process Automation or RPA will also become a dominating trend in 2019. RPA will drive to automate high-volume repeatable tasks, thus making them faster, more accurate, and less effort-intensive.

Conclusion:

2019 promises to be an exciting year in the world of software development. It will be interesting to see how these trends develop over the course of the year. Check back with us at the end of the year for a review of our predictions. And, do feel free to add more about the trends you think will dominate software development and testing in 2019.

 

The Role of AI In Software Testing

According to Gartner, by 2020, AI technologies will be pervasive in almost every new product and service and will also be a top investment priority for CIO’s. 2018 really was all about Artificial Intelligence. Tech giants such as Microsoft, Facebook, Google, Amazon and the like spent billions on their AI initiatives. We started noticing the rise of AI as an enterprise technology. It’s now clear how AI brings new intelligence to everything it touches by exploiting the vast sea of data at hand. Influential voices also started talking about the paradigm shift that this technology would bring to the world of software development. Of course, software testing too has not remained immune to the charms of AI.

Role: AI In Software Testing.

Role of AI In Software Testing

But first, Why do we Need AI for Software Testing?

It seems like we have only just firmly established the role of test automation in the software testing landscape and we must start preparing for further disruptions promised by AI! The rise of test automation was driven by development methodologies such as Agile and the need to ship bug and error-free, robust software products into the market faster. From there we have progressed into the era of daily deployments with the rise of DevOps. DevOps is pushing organizations to accelerate the QA cycle even further, to reduce test overheads, and to enable superior governance. Automating test requirement traceability and versioning are also factors that now need careful consideration in this new development environment.

The “surface area” of testing has also increased considerably. As applications interact with one another through API’s leveraging legacy systems, the complexity tends to increase as the code suites keep growing. As the software economy grows and enterprises push towards digital transformation, businesses now demand real-time risk assessment across the different stages of the software delivery cycle.

The use of AI in software testing could emerge as a response to these changing times and environments. AI could help in developing failsafe applications and to enable greater automation in testing to meet these expanded expectations from testing.

How will AI work in Software Testing?

As we move deeper into the age of digital disruption, the traditional ways of developing and delivering software are inadequate to fuel innovation. Delivery timelines are reducing but the technical complexity is rising. With Continuous Testing gradually becoming the norm, organizations are trying to further accelerate the testing process to bridge the chasm between development, testing, and operations in the DevOps environment.

  1. AI helps organizations achieve this pace of accelerated testing and helps them test smarter and not harder. AI has been called, “A field of study that gives computers the ability to learn without being explicitly programmed”. This being the case, organizations can leverage AI to drive automaton by leveraging both supervised and unsupervised methods.
  2. An AI-powered testing platform can easily recognize changed controls promptly. The constant updates in the algorithms will ensure that even the slightest changes can be identified easily.
  3. AI in test automation can be employed for object application categorizations for all user interfaces very effectively. Upon observing the hierarchy of controls, testers can create AI enabled technical maps that look at the graphical user interface (GUI) and easily obtain the labels for different controls.
  4. AI can also be employed effectively to conduct exploratory testing within the testing suite. Risk preferences can be assigned, monitored, and categorized easily with AI. It can help testers in creating the right heat maps to identify bottlenecks in processes and help in increasing test accuracy.
  5. AI can be leveraged effectively to identify behavioral patterns in application testing, defect analysis, non-functional analytics, analysis data from social media, estimation, and efficiency analysis. Machine Learning, a part of AI, algorithms can be employed to test programs and to generate robust test data and deep insights, making the testing process more in-depth and accurate.
  6. AI can also increase the overall test coverage and the depth and the scope of the tests as well. AI algorithms in software testing can be put to work for test suite optimization, enhancing UI testing, traceability, defect analysis, predicting the next test for queuing, determine pass/fail outcomes for complex and subjective tests, rapid impact analysis etc. Since 80% of all tests are repetitive, AI can free up the tester’s time and helps them focus on the more creative side of testing.

Conclusion:

Perhaps the ultimate objective of using AI in software testing is to aim for a world where the software will be able to test, diagnose, and self-correct. This could enable quality engineering and could further reduce the testing time from days to mere hours. There are signs that the use of AI in software testing can save time, money, and resources and help the testers focus their attention on doing the one thing that matters – release great software.

5 Most In-Demand Technology Skills

This is now a software-defined world. Almost every company today is a technology company. Every product, in some way, is a technology product. As businesses lean more heavily on technology and software, the software development and technology landscape become even more dynamic. Technology is in a constant state of flux, with one shiny new object outshining the one from yesterday. The stakeholders of software development, the testers, developers, designers etc. thus need to constantly re-evaluate their skills. In this environment of constant change, here are, in my opinion, the five most in-demand technology skills to possess today, and why?

demanding technology skills

  1. R: Owing to the advances in machine learning, the R programming language is having its coming of age moment now. This open source language has been a workhorse for sorting and manipulating large data sets and has shown its versatility in model building, statistical operations, and visualizations.

    R, over the years, has become a foundational tool in expanding AI to unlock large data blocks. As data became more dominant, R has made itself quite comfortable in the data science arena.

    In fact, this language is predicted to surpass the use of Python in data science as R, in contrast to Python, allows robust statistical models to be written in just a few lines. As the world falls more in love with data science it will also find itself getting closer to R.
  2. React: Amongst client-side technologies, React has been growing in popularity rapidly. While the number of frameworks based on JavaScript continues to increase, React still dominates this space. Open Sourced by Facebook in 2013, React has been climbing up the technology charts owing to its ease of use, high level of flexibility and responsiveness, its virtual DOM (document object model) capabilities, its downward data binding capabilities, the ease of enabling migrations, and light-weightiness.

    React is also winning in the NPM download race and has won the crown of the Best JavaScript framework of 2018. In the age of automation, React gives developers a framework that allows them to break down complex components and reuse codes to complete projects faster.

    Its unique syntax that allows HTML quotes, as well as HTML tag syntax, help in promoting construction of machine-readable codes. React also gives developers the flexibility to break down complex UI/UX development into simpler components and allows them to make every component intuitive. It also has excellent runtime performance.
  3. Swift: In 2017 we heard reports of the declining popularity of Swift. One of the main reasons for the same was a perceived preference among developers’ to use multiplatform tools. Swift, that is merely four years old, ranked 16 on the TIOBE index despite having a good start. The reason was mainly the changing methodologies in the mobile development ecosystem.

    However, in 2018 we seem to be witnessing the rise of Swift once again. According to a study conducted by analyst firm RedMonk, Swift tied with Object C at rank 10 in their January 2018 report. It fell one place in the June report, but that could be attributed to the lack of a server-side presence, something IBM has been working to rectify in keeping with its enterprise push.

    Once Swift became open source it has grown in popularity and has also matured as a language. With iOS apps proving to be more profitable than Android apps, we can expect more developers to switch to Swift. Swift is also finding its way into business discussions as enterprises look at robust iOS apps that offer performance as well as security.
  4. Test Automation: Organizations are racing to achieve business agility. This drive has promoted the rise of new development methodologies and the move towards continuous integration and continuous delivery. In this need for speed Test automation will continue to rise in prominence as it enables faster feedback. The push towards digital transformation in enterprises is also putting the focus on testing and quality assurance.

    I expect Shift-left testing to grow to hasten software development. Test automation is rapidly emerging as the enabler of software confidence. With the rising interest in new technologies like IoT and blockchain, test automation is expected to get a further push.

    The possible role of AI in testing is also something to look out for as AI could bring in more intelligence, validation, efficiency, and automation to testing. These could be exciting times for those in the testing and test automation space.
  5. UX: Statistics reveal that 90% of users stop using an application with a bad UX. 86% of users uninstall an app if they encounter problems with its functionality in design. UX or User Experience will continue to rise in prominence as it is the UX that earns users interest and ultimately their loyalty. The business value of UX will rise even further as we delve deeper into the app economy.

    The role of UX designers is becoming even more compelling as we witness the rise of AR, chatbots and virtual assistants. With the software products and services market becoming increasingly competitive, businesses have to focus heavily on UX design to deliver intuitive and coherent experiences to their users that drive usage and foster adoption.

It is an exciting time for us in the technology game. Innovation, flexibility, simplicity, reliability, and speed have become important contributors to software success. The key differentiator in these dynamic times may be the technology skills that you as an individual or as a technology-focused organization possess. To my mind, the skills that will help you stay ahead are those I’ve identified here.

The 5 Point Guide For A Successful DevOps Strategy

As the requirement for high-quality software in short time frames and restricted budgets increases, developers are looking for approaches that make building software a lot faster and more efficient. DevOps greatly helps in improving the software product delivery process; by bridging the gap between the development and operations teams, DevOps facilitates greater communication and collaboration, and improves service delivery, while reducing errors and improving quality. According to the State of Agile report, 58% of organizations embrace DevOps to accelerate delivery speed.

5 Point Guide to Devops Strategy

Tools for a successful DevOps Strategy

DevOps creates a stable operating environment and enables rapid software delivery through quick development cycles – all while optimizing resources and costs. However, before you embark on the DevOps journey, it is important to understand that since DevOps integrates people, processes, and tools together, more than tools and technology, it requires a focus on people and organizational change. Begin by driving an enterprise-wide movement – right from the top-level management down to the entry-level staff – and ensure everyone is informed of the value DevOps brings to the organization before integrating them together into cross-functional teams.

Next, selecting the right tools is critical to the success of your DevOps strategy; make sure the tools you select work with the cloud, support network, and IT resources and comply with the necessary security and governance requirements. Here’s your 5-point guide for developing a successful DevOps strategy and the tools you would need to drive sufficient value:

  1. Understand your Requirements: Although this would seem a logical first step, many organizations often make the DevOps plunge in haste, without sufficient planning. Start by understanding the solution patterns of the applications you plan to build. Consider all important aspects of software development including security, performance, testing, and monitoring — basically all of the core details. Use tools like Pencil, a robust prototyping platform, to gather requirements and create mockups. With hundreds of built-in shape collections, you can simplify drawing operations and enable easy GUI prototyping.
  2. Define your DevOps Process: Implementing a DevOps strategy might be the ideal thing to do, but understanding what processes you want to employ and what end result you are looking to achieve is equally important. Since DevOps processes differ from organization to organization, it is important to understand which traditional approaches to development and operations to let go of as you move to DevOps. Tools like GitHub can enable you to improve development efficiency and enjoy flexible deployment options, centralized permissions, innumerable integrations and more. GitHub allows you to host and review code, manage projects, and build quality software – moving ideas forward and learning all along the way.
  3. Fuel Collaboration: Collaboration is a key element of any DevOps strategy. It is only through continuous collaboration that you can develop and review code and stay abreast with all the happenings. With frequent and efficient collaboration, you can efficiently share workloads, enable frequent reviews, be informed of every update, resolve simple conflicts with ease, and improve the quality of your code. Collaboration tools like Jira and Asana enable you to plan and manage tasks with your team across the software development lifecycle. While Jira allows team members to effectively plan and distribute tasks, prioritize and discuss team’s work, and build and release great software together, Asana allows project leaders to assign responsibilities throughout the project; you can prioritize tasks, assign timelines, view individual dashboards and communicate on project goals.
  4. Enable Automated Testing: When developing a DevOps strategy, it is important to enable automated testing. Automated test scripts speed up the process of testing, and also improve the quality of your software by testing it thoroughly at each stage. By leveraging real-world data, they reflect production-level loads and identify issues in time. DevOps-friendly tools like Selenium are ideal for enabling automated testing. Since Selenium supports multiple operating systems and browsers, you can write test scripts in various languages including Java, Python, Ruby and more and can also extend test capability using additional test libraries.
  5. Continuously Monitor Performance: To get the most out of your DevOps strategy, measuring and monitoring performance is key. Given the fact that there will be hundreds of services and processes running in your DevOps environment, all of which cannot be monitored, the identification of the key metrics you want to track is vital. Tools like Jenkins can be used to continuously monitor your development cycles, deployment accuracy, system vulnerabilities, server health, and application performance. By quickly identifying problems, it enables you to integrate project changes more easily and deliver a functional product more quickly.

Improve Service Delivery

Implementing a DevOps strategy is not just about building high-quality software faster; it’s about driving a cultural shift across the organization to improve development processes and make it more efficient. Making the most of a switch to DevOps requires you to start with a new outlook, along with the use of new tools and new processes. By using the right tools at every stage, you can accelerate the product development process, meet time-to-market deadlines, and begin your journey towards improved service delivery and optimized costs.

Top 90 QA Interview Questions Answers

Let’s dive into Top 90 QA Interview Questions answers that we will recommend you while appearing for any QA interview.

QA interview question answers

  1. What is Software Quality Assurance (SQA)?
  2. Software quality assurance is an umbrella term, consisting of various planned process and activities to monitor and control the standard of whole software development process so as to ensure quality attribute in the final software product.

  3. What is Software Quality Control (SQC)?
  4. With the purpose similar to software quality assurance, software quality control focuses on the software instead to its development process to achieve and maintain the quality aspect in the software product.

  5. What is Software Testing?
  6. Software testing may be seen as a sub-category of software quality control, which is used to remove defects and flaws present in the software, and subsequently improves and enhances the product quality.

  7. Whether, software quality assurance (sqa), software quality control (sqc) and software testing are similar terms?
  8. No, but the end purpose of all is same i.e. ensuring and maintaining the software quality.

  9. Then, what’s the difference between SQA, SQC and Testing?
  10. SQA is a broader term encompassing both SQC and testing in it and ensures software development process quality and standard and subsequently in the final product also, whereas testing which is used to identify and detect software defects is a sub-set of SQC.

  11. What is software testing life cycle (STLC)?
  12. Software testing life cycle defines and describes the multiple phases which are executed in a sequential order to carry out the testing of a software product. The phases of STLC are requirement, planning, analysis, design, implementation, execution, conclusion and closure.

  13. How STLC is related to or different from SDLC (software development life cycle)?
  14. Both SDLC and STLC depict the phases to be carried out in a subsequent manner, but for different purpose. SDLC defines each and every phase of software development including testing, whereas STLC outlines the phases to be executed during a testing process. It may be inferred that STLC is incorporated in the SDLC phase of testing.

  15. What are the phases involved in the software testing life cycle?
  16. The phases of STLC are requirement, planning, analysis, design, implementation, execution, conclusion and closure.

  17. Why entry criteria and exit criteria is specified and defined?
  18. Entry and exit criteria is defined and specified to initiate and terminate a particular testing process or activity respectively, when certain conditions, factors and requirements is/are being met or fulfilled.

  19. What do you mean by the requirement study and analysis?
  20. Requirement study and analysis is the process of studying and analysing the testable requirements and specifications through the combined efforts of QA team, business analyst, client and stakeholders.

  21. What are the different types of requirements required in software testing?
  22. Software/functional requirements, business requirements and user requirements.

  23. Is it possible to test without requirements?
  24. Yes, testing is an art, which may be carried out without requirements by a tester by making use of his/her intellects possessed, acquired skills and gained experience in the relevant domain.

  25. Differentiate between software requirement specifications (SRS) and business requirement specification (BRS).
  26. SRS layouts the functional and non-functional requirements for the software to be developed whereas BRS reflects the business requirement i.e., the business demand of a software product as stated by the client.

  27. Why there is a bug/defect in software?
  28. A bug or a defect in software occurs due to various reasons and conditions such as misunderstanding or requirements, time restriction, lack of experience, faulty third party tools, dynamic or last time changes, etc.

  29. What is a software testing artifact?
  30. Software testing artifact or testing artifact are the documents or tangible products generated throughout the testing process for the purpose of testing or correspondence amongst the team and with the client.

  31. What are test plan, test suite and test case?
  32. Test plan defines the comprehensive approach to perform testing of the system and not for the single testing process or activity. A test case is based on the specified requirements & specifications define the sequence of activities to verify and validate one or more than one functionality of the system. Test suite is a collection of similar types of test cases.

  33. How to design test cases?
  34. Broadly, there are three different approaches or techniques to design test cases. These are

    • Black box design technique, based on requirements and specifications.
    • White box design technique based on internal structure of the software application.
    • Experience based design technique based on the experience gained by a tester.
  35. What is test environment?
  36. A test environment comprises of necessary software and hardware along with the network configuration and settings to simulate intended environment for the execution of tests on the software.

  37. Why test environment is needed?
  38. Dynamic testing of the software requires specific and controlled environment comprising of hardware, software and multiple factors under which a software is intended to perform its functioning. Thus, test environment provides the platform to test the functionalities of software in the specified environment and conditions.

  39. What is test execution?
  40. Test execution is one of the phases of testing life cycle which concerns with the execution of test cases or test plans on the software product to ensure its quality with respect to specified requirements and specifications.

  41. What are the different levels of testing?
  42. Generally, there are four levels of testing viz. unit testing, integration testing, system testing and acceptance testing.

  43. What is unit testing?
  44. Unit testing involves the testing of each smallest testable unit of the system, independently.

  45. What is the role of developer in unit testing?
  46. As developers are well versed with their lines of code, they are preferred and being assigned the responsibility of writing and executing the unit tests.

  47. What is integration testing?
  48. Integration testing is a testing technique to ensure proper interfacing and interaction among the integrated modules or units after the integration process.

  49. What are stubs and drivers and how these are different to each other?
  50. Stubs and drivers are the replicas of modules which are either not available or have not been created yet and thus they works as the substitutes in the process of integration testing with the difference that stubs are used in top bottom approach and drivers are used in bottom up approach.

  51. What is system testing?
  52. System testing is used to test the completely integrated system as a one system against the specified requirements and specifications.

  53. What is acceptance testing?
  54. Acceptance testing is used to ensure the readiness of a software product with respect to specified requirement and specification in order to get readily accepted by the targeted users.

  55. Different types of acceptance testing.
  56. Broadly, acceptance testing is of two types-alpha testing and beta testing. Further, acceptance testing can also be classified into following forms:

    • Operational acceptance testing
    • Contract acceptance testing
    • Regulation acceptance testing
  57. Difference between alpha and beta testing.
  58. Both alpha and beta testing are the forms of acceptance testing where former is carried out at development site by the QA/testing team and the latter one is executed at client site by the intended users.

  59. What are the different approaches to perform software testing?
  60. Generally, there are two approaches to perform software testing viz. Manual testing and Automation. Manual testing involves the execution of test cases on the software manually by the tester whereas automation process involves the usage of automation framework and tools to automate the task of test scripts execution.

  61. What is the advantage of automation over manual testing approach and vice-versa?
  62. In comparison to manual approach of testing, automation reduces the efforts and time required in executing the large amount of test scripts, repetitively and continuously for a longer period of time with accuracy and precision.

  63. Is there any testing technique that does not needs any sort of requirements or planning?
  64. Yes, but with the help of test strategy using check lists, user scenarios and matrices.

  65. Difference between ad-hoc testing and exploratory testing?
  66. Both ad-hoc testing and exploratory testing are the informal ways of testing the system without having proper planning & strategy. However, in ad-hoc testing, a tester is well-versed with the software and its features and thereby carries out the testing whereas in exploratory, he/she gets to learn and explore more about the software during the course of testing and thus tests the system gradually along with software understanding and learning throughout the testing process.

  67. How monkey testing is different from ad-hoc testing?
  68. Both monkey and ad-hoc testing are the informal approach of testing but in monkey testing, a tester does not requires the pre-understanding and detailing of the software, but learns about the product during the course of testing whereas in ad-hoc testing, tester has the knowledge and understanding of the software.

  69. Why non-functional testing is equally important to functional testing?
  70. Functional testing tests the system’s functionalities and features as specified prior to software development process. It only validates the intended functioning of the software against the specified requirement and specification but the performance of the system to function in the unexpected circumstances and conditions in real world environment at the users end and to meet customer satisfaction is done through non-functional testing technique. Thus, non-functional testing looks after the non-functional traits of the software.

  71. Which is a better testing methodology: black-box testing or white-box testing?
  72. Both black-box and white-box testing approach have their own advantages and disadvantages. Black-box testing approach enables testers to externally test the system on the basis of specified requirement and specification and does not provide the scope of testing the internal structure of the system, whereas white-box testing methodology verify and validates the software quality through testing of its internal structure and working.

  73. If black-box and white-box, then why gray box testing?
  74. Gray box testing is a third type of testing and a hybrid form of black-box and white-box testing approach, which provides the scope of externally testing the system using test plans and test cases derived from the knowledge and understanding of internal structure of the system.

  75. Difference between static and dynamic testing of software.
  76. The primary difference between static and dynamic testing approach is that the former does not involves the execution of code to test the system whereas latter approach requires the code execution to verify and validate the system quality.

  77. Smoke and Sanity testing are used to test software builds. Are they similar??
  78. Although, both smoke and sanity testing is used to test software builds but smoke testing is used to test the initial build which are unstable whereas sanity tests are executed on relatively stable builds which had undergone multiple time through regression testing.

  79. When, what and why to automate?
  80. Automation is preferred when the execution of tests needs to be carried out repetitively for a longer period of time and within the specified deadlines. Further, an analysis of ROI on automation is desired to analyse the cost-benefit model of the automation. Preferably functional, regression and functional tests may be automated. Further, tests which requires accuracy and precision, and is time-consuming may be considered for automation, including data driven tests also.

  81. What are the challenges faced in automation?
  82. Some of the common challenges faced in the automation are

    • Initial cost is very high along with the maintenance costs. Thus, requires proper analysis to assess ROI on automation.
    • Increased complexities.
    • Limited time.
    • Demands skilled tester, having appropriate knowledge of programming.
    • Automation training cost and time.
    • Selection of right and appropriate tools and frameworks.
    • Less flexible.
    • Keeping test plans and cases updated and maintained.
  83. Difference between retesting and regression testing.
  84. Both retesting and regression testing is done after modification in software features and configuration to remove or correct the defect(s). However, retesting is done to validate that the identified defects has been removed or resolved after applying patches while regression testing is done to ensure that the modification in the software doesn’t impacts or affects the existing functionalities and originality of the software.

  85. How to categorize bugs or defects found in the software?
  86. A bug or a defect may be categorized on the priority and severity basis, where priority defines the need to correct or remove defect, from business perspective, whereas severity states the need to resolve or eliminate defect from software requirement and quality perspective.

  87. What is the importance of test data?
  88. Test data is used to drive the testing process, where diverse types of test data as inputs are provided to the system to test the response, behaviour and output of the system, which may be desirable or unexpected.

  89. Why agile testing approach is preferred over traditional way of testing?
  90. Agile testing follows the agile model of development, which requires no or less documentation and provides the scope of considering and implementing the dynamic and changing requirements along with the direct involvement of client or customer to work on their regular feedbacks and requirements to provide software in multiple and short iterative cycles.

  91. What are the parameters to evaluate and assess the performance of the software?
  92. Parameters which are used to evaluate and assess the performance of the software are active defects, authored tests, automated tests, requirement coverage, no. of defects fixed/day, tests passed, rejected defects, severe defects, reviewed requirements, test executed and many more.

  93. How important is the localization and globalization testing of a software application?
  94. Globalization and localization testing ensures the software product features and standards to be globally accepted by the world wide users and to meet the need and requirements of the users belonging to a particular culture, area, region, country or locale, respectively.

  95. What is the difference between verification and validation approach of software testing?
  96. Verification is done throughout the development phase on the software under development whereas validation is performed over final product produced after the development process with respect to specified requirement and specification.

  97. Does test strategy and test plan define the same purpose?
  98. Yes, the end purpose of test strategy and test plan is same i.e. to works as a guide or manual to carry out the software testing process, but still they both differs.

  99. Which is better approach to perform regression testing: manual or automation?
  100. Automation would provide better advantage in comparison to manual for performing regression testing.

  101. What is bug life cycle?
  102. Bug or Defect life cycle describes the whole journey or the life of a defect through various stages or phases, right from when it is identified and till its closure.

  103. What are the different types of experience based testing techniques?
  104. Error guessing, checklist based testing, exploratory testing, attack testing.

  105. Whether a software application can be 100% tested?
  106. No, as one of the principles of software testing states that exhaustive testing is not possible.

  107. Why exploratory testing is preferred and used in the agile methodology?
  108. As agile methodology requires the speedy execution of the processes through small iterative cycles, thereby calls for the quick, and exploratory testing which does not depends on the documentation work and is carried out by tester through gradual understanding of the software, suits best for the agile environment.

  109. Difference between load and stress testing.
  110. The primary purpose of load and stress testing is to test system’s performance, behaviour and response under different varied load. However, stress testing is an extreme or brutal form of load testing where a system under increasing load is subjected to certain unfavourable conditions like cut down in resources, short or limited time period for execution of task and various such things.

  111. What is data driven testing?
  112. As the name specifies, data driven testing is a type of testing, especially used in the automation, where testing is carried out and drive by the defined sets of inputs and their corresponding expected output.

  113. When to start and stop testing?
  114. Basically, on the availability of software build, testing process starts. However, testing may be started early with the development process, as soon as the requirements are gathered and available. Moreover, testing depends upon the requirement of the software development model like in waterfall model, testing is done in the testing phase, whereas in agile testing is carried out in multiple and short iteration cycle.

    Testing is an infinite process as it is impossible to make a software 100% bug free. But still, there are certain conditions specified to stop testing such as:

    • Deadlines
    • Complete execution of the test suites and scripts.
    • Meeting the specified exit criteria for a test.
    • High priority and severity bugs are identified and resolved.
    • Complete testing of the functionalities and features.
  115. Whether exhaustive software testing is possible?
  116. No

  117. What are the merits of using the traceability matrix?
  118. The primary advantage of using the traceability matrix is that it maps the all the specified requirements with that to test cases, thereby ensures complete test coverage.

  119. What is software testability?
  120. Software testability comprises of various artifacts which gives the estimation about the efforts and time required in the execution of a particular testing activity or process.

  121. What is positive and negative testing?
  122. Positive testing is the activity to test the intended and correct functioning of the system on being fed with valid and appropriate input data whereas negative testing evaluates the system’s behaviour and response in the presence of invalid input data.

  123. Brief out different forms of risks involved in software testing.
  124. Different types of risks involved in software testing are budget risk, technical risk, operational risk, scheduled risk and marketing risk.

  125. Why cookie testing?
  126. Cookie is used to store the personal data and information of a user at server location, which is later used for making connections to web pages by the browsers, and thus it is essential to test these cookies.

  127. What constitutes a test case?
  128. A test case consists of several components. Some of them are test suite id, test case id, description, pre-requisites, test procedure, test data, expected results, test environment.

  129. What are the roles and responsibilities of a tester or a QA engineer?
  130. A QA engineer has multiple roles and is bounded to several responsibilities such as defining quality parameters, describing test strategy, executing test, leading the team, reporting the defects or test results.

  131. What is rapid software testing?
  132. Rapid software testing is a unique approach of testing which strikes out the need of any sort of documentation work, and motivates testers to make use of their thinking ability and vision to carry out and drive the testing process.

  133. Difference between error, defect and failure.
  134. In the software engineering, error defines the mistake done by the programmers. Defect reflects the introduction of bugs at production site and results into deviation in results from its expected output due to programming mistakes. Failure shows the system’s inability to execute functionalities due to presence of defect. i.e. defect explored by the user.

  135. Whether security testing and penetration testing are similar terms?
  136. No, but both testing types ensure the security mechanism of the software. However, penetration testing is a form of security testing which is done with the purpose to attack the system to ensure not only the security features but also its defensive mechanism.

  137. Distinguish between priority and severity.
  138. Priority defines the business need to fix or remove identified defect whereas severity is used to describe the impact of a defect on the functioning of a system.

  139. What is test harness?
  140. Test harness is a term used to collectively define various inputs and resources required in executing the tests, especially the automated tests to monitor and assess the behaviour and output of the system under different varied conditions and factors. Thus, test harness may include test data, software, hardware and many such things.

  141. What constitutes a test report?
  142. A test report may comprise of following elements:

    • Objective/purpose
    • Test summary
    • Logged defects
    • Exit criteria
    • Conclusion
    • Resources used
  143. What are the test closure activities?
  144. Test closure activities are carried out the after the successful delivery or release of the software product. This includes collection of various data, information, testwares pertaining to software testing phase so as to determine and assess the impact of testing on the product.

  145. List out various methodologies or techniques used under static testing.
    • Inspection
    • Walkthroughs
    • Technical reviews
    • Informal reviews
  146. Whether test coverage and code coverage are similar terms?
  147. No, code coverage amounts the percentage of code covered during software execution whereas test coverage concerns with the test cases to cover specified functionality and requirement.

  148. List out different approaches and methods to design tests.
  149. Broadly, there are different ways along with their sub techniques to design test cases, as mentioned below

    • Black Box design technique- BVA, Equivalence Partitioning, use case testing.
    • White Box design technique- statement coverage, path coverage, branch coverage
    • Experience based technique- error guessing, exploratory testing
  150. How system testing is different to acceptance testing?
  151. System testing is done with the perspective to test the system against the specified requirements and specification whereas acceptance testing ensures the readiness of the system to meet the needs and expectations of a user.

  152. Distinguish between use case and test case.
  153. Both use case and test case is used in the software testing. Use case depicts and defines the user scenarios including various possible path taken by the system under different conditions and circumstances to execute a particular task and functionality. On the other side, test case is a document based on the software and business requirements and specification to verify and validate the software functioning.

  154. What is the need of content testing?
  155. In the present era, content plays a major role in creating and maintaining the interest of the users. Further, the quality content attracts the audience, makes them convinced or motivated over certain things, and thus is a productive input for the marketing purpose. Thus, content testing is a must testing to make your software content suitable for your targeted users.

  156. List out different types of documentation/documents used in the software testing.
    • Test plan.
    • Test scenario.
    • Test cases.
    • Traceability Matrix.
    • Test Log and Report.
  157. What is test deliverables?
  158. Test deliverables are the end products of a complete software testing process- prior, during and after the testing, which is used to impart testing analysis, details and outcomes to the client.

  159. What is fuzz testing?
  160. Fuzz testing is used to discover coding flaws and security loopholes by subjecting system with the large amount of random data with the intent to break the system.

  161. How testing is different with respect to debugging?
  162. Testing is done with the purpose of identifying and locating the defects by the testing team whereas debugging is done by the developers to fix or correct the defects.

  163. What is the importance of database testing?
  164. Database is an inherited component of a software application as it works as a backend system of the application and stores different types of data and information from multiple sources. Thus, it is crucial to test the database to ensure integrity, validity, accuracy and security of the stored data.

  165. What are the different types of test coverage techniques?
    • Statement Coverage
    • Branch Coverage
    • Decision Coverage
    • Path Coverage
  166. Why and how to prioritize test cases?
  167. Due to abundance of test cases for the execution within the given testing deadline arises the need to prioritize test cases. Test prioritization involves the reduction in the number of test cases, and selecting & prioritizing only those which are based on some specific criteria.

  168. How to write a test case?
  169. Test cases should be effective enough to cover each and every feature and quality aspect of software and able to provide complete test coverage with respect to specified requirements and specifications.

  170. How to measure the software quality?
  171. There are certain specified parameters, namely software quality metrics which is used to assess the software quality. These are product metrics, process metrics and project metrics.

  172. What are the different types of software quality model?
    • Mc Call’s Model
    • Boehm Model
    • FURPS Model
    • IEEE Model
    • SATC’s Model
    • Ghezzi Model
    • Capability Maturity Model
    • Dromey’s quality Model
    • ISO-9126-1 quality model
  173. What different types of testing may be considered and used for testing the web applications?
    • Functionality testing
    • Compatibility testing
    • Usability testing
    • Database testing
    • Performance testing
    • Accessibility testing
  174. What is pair testing?
  175. Pair testing is a type of ad-hoc testing where pair of testers or tester and developer or tester & user is being formed which are responsible for carrying out the testing of the same software product on the same machine.

Hope these 90 QA Questions has provided you a complete overview of the QA process. We wish above QA interview questions will help you clear your next QA interview. Do share your feedback with us @ [email protected] and let us know how these QA questions have helped you during your QA interview.

Ultimate Guide to Functional Test Automation

Testing your newly-designed code for bugs and malfunction is an important part of the development process. After all, your application or piece of code will be used in different systems, environments, and scenarios after shipping.

According to statistics, 36% of developers claim that they will not implement any new coding techniques or technologies in their work at least for the coming year. This goes to show how fast the turnaround times are in the software development world.

It’s often better to ship a slightly less ambitious but functional product than a groundbreaking, unstable one. However, you can achieve both if you automate your quality assurance processes carefully. Let’s take a look at how and why you should automate your functional tests for a quick and valuable feedback during the coding process.

Ultimate guide to functional test automation

Benefits of Functional Testing & Automation:

  • Maintaining your Reputation:
    Whether you are a part of a large software development company or an independent startup project, your reputation plays a huge role in the public perception of your work. Research shows that 17% of developers agree that unrealistic expectations are the biggest problem in their respective fields. Others state that lack of goal clarity, prioritization, and a lack of estimation also add to the matter.
    There is always a dissonance between managers and developers, which leads to crunch periods and very quick product delivery despite a lack of QA testing. Automated functional testing of your code can help you maintain a professional image by shipping a working product at the end of the development cycle.
  • Controlled Testing Environment:
    One of the best parts of in-house testing is the ability to go above and beyond with how much stress you put on your code.
    For example, you can strain the application or API with as much incoming data and connections as possible without the fear of the server crashing or some other anomaly. While you can never predict how your code will be used in practice, you can assume as many scenarios as possible and test for those specific scenarios.

  • Early Bug Detection:
    Most importantly, functional test automation allows for constant, day-to-day testing of your developed code. You can detect bugs, glitches, and data bottlenecks very quickly in doing so.
    That way, you will detect problems early in the development stage without relying on test group QA which will or will not come across practical issues. The bugs you discover early on can sometimes steer your development process in an entirely different direction, one that you would be oblivious to without automated, repeated testing.
  1. Is Your Test’s Automation Necessary?
    Before you decide to design your automated functionality test, it’s important to gauge its necessity in the overall scheme of things. Do you really need an automated test at this moment or can you test your code’s functionality manually for the time being?
    The reason behind this question is simple – the use of too much automated testing can have adverse effects on the data you collect from it. More importantly, test design takes time and careful scripting, both of which are valuable in the project’s development process. Make sure that you are absolutely sure that you need automated tests at this very moment before you step into the scripting process.
  2. Separate Testing from Checking:
    Testing and checking are two different things, both of which correlate with what we said previously. In short, when you “check” your code, you will be fully aware, engaged, and present for the process. Testing, on the other hand, is automated and you will only see the end-results as the final data rolls in.
    Both testing and checking are important in the QA of your project, but they can in no way replace one another. Make sure that both are implemented in equal measure and that you double-check everything that seems off or too good to be true manually.
  3. Map out the Script Fully:
    Running a partial script through your code won’t bring any tangible results to the table. Worse yet, it will confuse your developers and lead to even more crunch time. Instead, make sure that your script is fully written and mapped out before you put it into automated testing.
    Make sure that the functional test covers each aspect of your code instead of opting for selective testing. This will ensure that the code is tested for any conflicts and compatibility issues instead of running a step-by-step test.
  4. Multiple Tests with Slight Variations:
    What you can do instead of opting for several smaller tests is to introduce variations into your functionality test script. Include several variations in terms of scenarios and triggers which your code will go through in each testing phase.
    This will help you determine which aspects of your project need more polish and which ones are good as they are. Repeated tests with very small variations in between are a great way to vent out any dormant or latent bugs which can rear their head later on. Avoid unnecessary post-launch bug fixes and last-minute changes by introducing a multi-version functionality test early on.
  5. Go for Fast Turnaround:
    While it is important to check off every aspect of your code in the functional testing phase, it is also important to do so in a timely manner. Don’t rely on overly-complex or long tests in your development process.
    Even with automation and high-quality data to work with afterward, you will still be left with a lot of analysis and rework to be done as a result. Design your scripts so that they trigger every important element in your code without going into full top-to-bottom testing each time you do so. That way, you will have a fast and reliable QA system available for everyday coding – think of it as your go-to spellcheck option as you write your essay.
  6. Identify & Patch Bottlenecks:
    Lastly, it’s important to patch out the bottlenecks, bugs, and glitches you receive via the functional test you automated. Once these problems are ironed out, make sure to run your scripts again and check if you were right in your assertion.
    Running the script repeatedly without any fixes in between runs won’t yield any productive data. As a result, the entire process of functional test automation falls flat due to its inability to course-correct your development autonomously.

In Summation

Once you learn what mistakes are bound to happen again and again, you will also learn to fix them preemptively by yourself without the automated testing script. Use the automation feature as a helpful tool, not as a means to fix your code (which it won’t do by itself).

Patch out your glitches before moving forward and closer to the official launch or delivery of your code to the client. The higher the quality of work you deliver, the better you will be perceived as a professional development firm. It’s also worth noting that you will learn a lot as a coder and developer with each bug that comes your way.

Author: Elisa Abbott is a freelancer whose passion lies in creative writing. She completed a degree in Computer Science and writes about ways to apply machine learning to deal with complex issues. Insights on education, helpful tools, and valuable university experiences – she has got you covered;) When she’s not engaged in assessing translation services for PickWriters you’ll usually find her sipping a cappuccino with a book.

Are we set for the Blockchain Age in Data Storage?

Although Blockchain came into the limelight with the cryptocurrency bitcoin, in the last year or so, companies have become increasingly aware of how Blockchain can bring about transformation across industries. With the cloud storage market expected to grow to $88.91 billion by 2022, the decentralized storage industry is rapidly gaining popularity, and Blockchain will be critical to its success. Since data storage – especially critical financial data – is always vulnerable to security breaches, migrating data from private data centers onto public Blockchains can help enterprises decentralize storage, thereby enhancing availability, scalability, and security of data.

Blockchain Age in Data Storage

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Current Challenges:

It is not hard to imagine the ever-increasing volume of financial data that is being generated. Data, which will also then have to be managed, stored and analyzed for effective business decision-making. Connected devices, mobile apps, and the increasing need to share data across businesses are all contributing to the increasing demand for storage that is highly available, scalable, and secure.

Businesses that are looking to launch new, data-driven applications face a sea of challenges with respect to time, effort, and management to provision new datasets and databases.

Traditional cloud storage networks are also known to come with latency challenges. Since most of the time, the data that gets stored in a data center will not be in the same location as the business, delays in delivery are the norm – and that doesn’t work well in the financial context where delays of milliseconds can cause huge losses.

What’s more, the need for large databases also necessitates the need for managing large data centers, that require frequent temperature control, periodic updating, and rigorous upkeep -all expensive.

In addition, the road towards a richer, more data-centric way of working is further challenged by a global phenomenon of data breaches from centralized data centers. The outcome is worrisome – the growing storage needs of businesses are driving extraordinarily large volumes of data to be stored in centralized databases.

This creates risk at a scale never seen before. This necessitates the need for de-centralizing data storage, that can not only minimize the risk of a complete shutdown but also ensure efficiency and transparency of data storage.

The Benefits of Decentralized Storage:

As most current cloud-based databases are highly centralized, they are tempting targets for data breaches. Cloud Storage Companies do have several mechanisms in place to avoid the loss of data, such as dispersing duplicate files across various data centers to avoid a breach. That said, decentralizing storage would more or less eliminate the risk and repercussions of disruptions.

Although current networks need to evolve in order to accommodate such decentralized storage infrastructure, the day is not far when data will be supported by a network of decentralized nodes in a more user-friendly and cost-effective manner than the current, central database solutions.

Decentralized storage works by distributing the data across a network of nodes, thereby reducing the strain on a single node or database. Since it utilizes geographically distributed nodes, decentralized storage can avert such catastrophes and ensure the company’s data is always protected. As data is stored across hundreds of individual nodes, intelligently distributed across the globe, no single entity can control access – thus improving security and decreasing costs.

Any attack or outage at a single point will not result in a domino effect, as other nodes in other locations will continue to function without interruption. The distributed nature of these nodes also makes decentralized storage highly scalable, as companies can leverage the power of the network and achieve better up-time.

The Role of Blockchain:

Although one of the biggest achievements of the Internet era has undoubtedly been cloud data storage, it is already under threat of being replaced by Blockchain storage technology. As the need for decentralized storage becomes more and more relevant, the storage industry is looking to make the most of Blockchain’s distributed ledger technology.

Blockchain paves the way for user-centric storage networks, where companies can move data from the current centralized databases to Blockchain data storage, and benefit from a more agile, customizable system. Because storage gets distributed across nodes, companies can enjoy a better speed of retrieval and redundancy by accessing data from the node that is closest to them.

With such attributes that meet the practical demands of storing high volumes of data, Blockchain will partition databases along logical lines that can only be accessed by a decentralized application using a unique key. Such a decentralized network of storage nodes not only reduces latency but also increases the speed by retrieving data in parallel from the nearest and fastest node.

And because there are so many geographically dispersed nodes in a network, the reliability and scalability of decentralized storage are greater. What’s more, since the devices in the nodes aren’t owned or controlled by a single vendor but by several individuals, the availability and reliability of data are improved even further.

The Way Forward:

As industries battle issues of the security and confidentiality of data, the evolution of Blockchain has come like a boon. Touted as a technology with the potential to transform every industry, Blockchain could be particularly beneficial in the data storage game.

By improving business efficiency and bringing transparency in how enterprises store business data, Blockchain is poised to offer myriad benefits such as shared control of data, easy auditing, and secure data exchange. While it may take time for Blockchain to become the default choice for businesses looking to meet their ever-increasing storage needs, it won’t be long before the world opts for this secure, efficient, and scalable solution in an increasingly data-starved world. Are you Blockchain ready?

The Role of Big Data in Mobile App Development

With 4.57 billion mobile phone users in the world right now, the mobile app development industry is also at its pinnacle. With every company building mobile apps to address external as well as internal customers, there is a pressing need to keep pace with rapidly changing market trends, technology advances, and customer needs. One sure-shot way of out-performing the competition and achieving success is by letting data drive your decisions. Big data can enable you to unearth hidden patterns and customer preferences and you can lean on these to develop state-of-the-art mobile apps. Here’s how big data can play a major role in mobile app development.

Role of big data in mobile app development

  1. Understand Customer Needs: A great mobile app is not one which looks stunning but one which meets the needs of users. Using big data, you can analyze the overwhelming volume of data that users generate on a regular basis and convert it into relevant insights. By understanding how users from different backgrounds, age groups, lifestyles, and geographies relate, react, and interact with mobile apps, you can formulate ideas for new and innovative apps and boost the capabilities of existing ones. Uber uses big data in a big way to improve its customer service; when a customer requests for a cab, Uber analyzes real-time traffic conditions, availability of a driver nearby, estimated time for the journey, etc. and provides a time and cost estimate for improved engagement.
  2. Drive User Experience Analysis: In addition to understanding customer needs, mobile app development also requires you to understand how users use your app. Using big data, you can conduct detailed user experience analysis, get a comprehensive 360-degree view of usage and the user experience, evaluate the engagement for each feature or page, and determine the most sought-after features as well as pain points. You can understand which elements of your mobile app make users spend more time and which cause them to leave. You can then use this information to create a list of the very features that users demand, plan for changes or modifications in the design, improve user experience, and maximize engagement.
  3. Get Access to Real-time Data: Businesses today have to remain in touch with changing trends to stay ahead of the race. Big data helps a great deal in keeping up with the times. By examining real-time data, you can take real-time, data-driven decisions to improve customer satisfaction and bring in higher profit. Using big data, Fitbit tracks real-time health data including sleep, eating, and activity habits to enable better lifestyle choices. The data gathered by Fitbit not only helps individuals become healthier, but it also provides doctors and healthcare practitioners with a clear picture of overall health and habits across a wider population.
  4. Build the Right Marketing Strategies: With a pool of data about user behavior including their likes, dislikes, needs, expectations, and more, you can build the right marketing strategies around how, when and where to target your audience. You can make better decisions of all types, from what type of push notifications to send and what strategy to use in increasing engagement. Using big data, you can analyze users’ demographic data, purchase patterns, and social behavior to modify your marketing messages according to their current interests. By building the right strategies, you can drive adoption, fuel engagement, increase satisfaction and ultimately, grow app revenue.
  5. Enable Personalization: Big data also enables you to optimize search and make it more intuitive and less cumbersome for users. By analyzing data from customer queries, you can prioritize results, deliver better and more contextual experience that matter the most to a particular user. You can also group data and features to provide smarter self-service for immediate answers. Amazon uses big data to enable predictive analysis and offers product suggestions based on a user’s previous purchase history, products they have viewed or liked as well as trending products. By integrating recommendations across the buying cycle – from product discovery until checkout, Amazon delivers the most relevant products and delivers a personalized shopping experience to each shopper.

Drive Revenue:

In a highly mobile world today, the mobile app has become the centerpiece of all communication strategies for every business. It is estimated that the mobile app market will reach $189 billion by 2020. Although thousands of companies across the world are building mobile apps every single day, it is through technologies like big data that you can really boost app-performance and fuel user engagement. Big data puts real-time data to work to offer personalized experiences that cater to the needs of the users in the most effective manner. If the mobile is central to your go-to-market strategy, its time you made the most of big data to build better mobile apps that drive value and revenue.