Thanks to Big Data, Customer Experience is Transforming. Here is How

What is it that makes an Amazon or Netflix so popular among people?

Why is it that we go to a seemingly interesting website and then quickly leave it?

The answer may lie in the experience. You go to the Netflix app because you want to be entertained. You open the Amazon website because you want to compare prices and order a few things. Now, imagine if the design of the website or the app was confusing. What if Netflix did not give you personalized recommendations? What if Amazon did not categorize its items? You might waste a lot of time looking for what you want and might end up exiting the website or the app within a few seconds. For a digital-first company, that spells bad news.

Gone are those days when customer experience was just a way to stand apart from the competitors. Today, for apps and for software products, it is a necessity to survive in the hyper-competitive market. 86% of customers today are willing to pay a premium for excellent customer experience. A study by Walker suggests that in 2020, customer experience will overtake price and product as the key differentiator.

So, how do you create an experience that will make your customers stay on your platform?

To begin with, how do you even determine what good experience means to your customer?

The answer may lie in big data.

Thanks to Big Data, Customer Experience is Transforming. Here is How

How has Big Data Helped Companies to Improve Customer Experience?

Companies have increasingly started relying on data to find ways to make the customer’s experience frictionless.

However, with terabytes and zettabytes of data generated every year, companies find it hard to collect, analyze, and interpret the data correctly.That’s when they use technology like big data.

Big data extracts information from large data sets systematically to enable businesses to make informed decisions.

Here’s how.

  1. Helps in Understanding the Behavior of the Customers:
    When you operate in a physical retail space, it is easier to observe the behavior of people and provide them what they need. But how do you emulate the same thing online? That’s where big data helps. With big data and analytics, you can find out how people interact with your product or website, how they prefer to engage, what prevents them from taking action?
    You can mine insights from customer support queries and online forum responses about what irks the customer? What features are they looking for? Understanding the behavior of the customer will enable you to make improvements to your product in line with the needs of the customer.
  2. Helps in Rectifying Customer Pain Points:
    Taking the previous point further, big data helps you to rectify the issues customers face while using your product. Let’s take Amazon for instance. What made Amazon introduce the 1-click ordering feature on their mobile app?
    It was not a random decision. Amazon realized that most customers backed out on having to enter all their information manually during the checkout process. With the 1-click option, the users can place an order without entering any details. This saves their time and increases conversions for Amazon.
    Once you gather information on what prevents your customers from deriving the necessary value from your product, your UX and UI designers can rectify the design or you can take the call to add a much-needed feature.

  3. Helps in Improving Visualization of the Website or App:
    To design a great product, you have to determine what you want the customers to do and then create the interface such that it helps them do that. Visualizing such a design might look simple, but if you want the design to be effective, you have to turn to big data. Let’s take Instagram’s example.
    Data revealed to the widely popular photo-sharing app that people were unable to find relevant images and videos on their feeds. So, they altered the feed from reverse-sequential order to display posts that they thought people would like to see, like, and share. This helped them to customize the data feed for their users and led to more popularity.
    The data collected through photos and videos let visitors discover new interests relevant to them. So, if you are planning to revamp the design of your product, read the data you collect from your customers and mine those insights to improve the usability.
  4. Removes all guesswork and leads to data-backed experiments:
    Every product change will cost money for your company. It could also earn you bouquets or brickbats depending upon how your customers react to the change. So, if you want to experiment with your UX or UI, conduct data-backed experiments. Conduct usability tests on a small batch of loyal users to know their feedback on the changes and use those insights to improve or modify the design.
    Turn to predictive analytics to get a sense of what could happen. This saves time and money and helps you focus on strategies that have a greater chance of working.
  5. Creates personalized interface for every user:
    Think of the Amazon homepage showing recommendations of products to buy or the personalized content recommendations by Netflix. Conditioned by the experience provided by apps like these, even enterprise and business users are demanding greater levels of customization.
    And the app and product experience can be so much more meaningful to your users too,thanks to big data. Analytics helps you bring that relevance in your products that encourages higher engagement.

Conclusion:

Until a few years ago, software products never thought of combining art and science to improve the customer experience. Today, customer experience has become more data-focused and customer-centric. And all credit for this significant shift must go to big data and analytics. Analytics has enabled companies to adopt science to create personalized experiences that make every customer feel special.

Angular vs Reactjs: Our Updated View

Our previous article touched upon the key features of AngularJS and ReactJS. But it’s been a while since then. Technology never stands still. There is now a considerable debate over which of those is better suited for front-end development. And here we are again with an updated account of what differentiates the two technologies, and which could be the better option for specific situations.

Web development is progressing at lightning speed. Something that was hot and relevant in 2017 may be seen as archaic today. Today, users have even more control and power over what they want. Product and app companies are being forced to frantically try and stay on-trend while meeting those growing demands.

In the software and mobile app universe, two technologies are being spoken of far and wide- Angular and ReactJS

While Angular is an MVC framework, React is a JS library that handles only the Views in an MVC framework. Despite, or perhaps because of, some foundational differences between the two technologies, there are several debates about the capabilities and preference of one over another. 

angular vs reactjs

Angular handles many functionalities out of the box and is centered around the concept of an application. React is more lightweight and does not handle quite as many functionalities out of the box. It comes with the concept of components, each with their own properties.

With that snippet of an introduction of both technologies, let’s try to (again) nail down the differences between them to help you pick one best suited to your unique needs.

Angular vs. ReactJS

With several out-of-the-box functions, Angular can help get you started quickly without feeling overwhelmed or intimidated by choices. Usually, developers feel at home quicker and switch between roles more comfortably with Angular.

For the web app-focused folks, while all Javascript frameworks have SEO capabilities, ReactJS does a particularly great job here. Run ReactJS on the server and the virtual DOM will be returned to your browser as a consistent web page.

Earlier, Angular was built to offer ease of software development, particularly through the use of modules. In that light, Angular also provides ease of testing, another advantage of the technology.

ReactJS Native is more focused around UI, unlike Angular. It allows for a more responsive interface with Javascript communications between the Native environment of a device and the framework. Therefore, it impacts the app’s load time and keeps it running seamlessly without interference.

Use cases of Angular vs. ReactJS

Use ReactJS when there is a lot of dynamic content in your software or applications. Top brands such as Facebook, Netflix, Uber, Dropbox, PayPal, Flipkart, and Instagram prefer ReactJS to drive their apps for this dynamic nature.

ReactJS could also be the choice for you when you plan on expanding the functionality of your mobile app in the near future.

On the other hand, choose Angular if you are just starting with app development, and want to be done quickly. Angular may also be the right choice out of the two when you are looking for a robust and well-maintained framework. Angular may work better when you have a team of experienced developers with a good hold on TypeScript.

Performance of Angular vs. ReactJS

React is racing ahead in the popularity charts for its fantastic rendering speed. The technology derives its name from its ability to react to change with minimal delay. In response, Angular 2 has been tried to improve its performance by modifying its ‘change detection’ algorithm.

When it comes to smaller applications, both technologies stand neck to neck. However, when apps get more complex and larger in size, React outperforms Angular. React can also be easily combined with Redux or Flux to build bigger applications.

React developers also have the benefit of a built-in virtual DOM feature that allows a server to save a light DOM tree, leading to less loading time on browsers and high performance.

The Learning Curve of  Angular vs. ReactJS

One of the distinguishing factors of ReactJS is that it is easy for anyone to learn. Since it is purely based on JavaScript, even newbies can familiarize themselves with the technology in no time. The learning curve associated with React is shallower and better suits beginning developers while Angular comes with a steep learning curve. This is essentially because developers are expected to be well-versed with the additional dependency of TypeScript, a statically typed programming language from Microsoft.

Why Choose Angular vs. ReactJS?

Choose Angular for its component structure, where you can utilize its components with several frameworks without having to stretch. Angular binding permits you to build a simple-to-maintain, logical connection between the model and the data view. It also allows for smooth testing and the flexibility of choosing your preferred environment for app development.

Choose ReactJS for its Virtual DOM, which increases the speed of the framework. Also, for its Simple State Machine components that allow you to modify the state of an object and apply all updates with ease. Developers also love the fact that the view is not disparate from the logic in ReactJS. That, and it’s fast to learn.

While there is no black and white here, the situation your product or app is in will help you make an educated decision on whether Angular or ReactJS would better suit your needs. For further assistance, reach out to us, and we will help you define the best roadmap for your application development needs.

The Special Role of Regression Testing in Agile Development

Presumably, everyone here who has developed products knows that regression testing is done to validate the existing code after a change in software. Unlike most other testing, it validates that nothing got broken in the already existing functionality of the software product even as changes were made to other parts. In a nutshell, the aim is to confirm that the product isn’t affected by the addition of any new features or bug fixes. Often, older test cases are, re-executed for reassurance that there were no ill-effects of changes.

role of regression in agile development

Regression testing is necessary for all product development where the product is evolving, that is, in effect for all products!

Which Brings is to Agile Software Development?

The Agile method calls for rapid product iterations and frequent releases. Obviously, this includes shorter and more frequent testing cycles. This is to ensure that the quality of the output of the sprints is intact whenever the software is released. These constant churns call for a massive focus on regression testing.

A sound regression testing strategy mainly helps the teams focus on new functionalities and maintain stability as the product increments take place. It makes sure that the earlier release and the new code are both in-sync. This is how the software’s functionality, quality, and performance remain intact even after going through several modifications.

To put things into perspective – the Agile method is all about iterative development and regression testing is all about focusing on the effects that occur due to that iterative new development.

What Makes Regression Testing Special in Agile Development?

  • Helps Identify Issues Early– One of the ways in which Agile teams build their regression testing strategy is to identify the improvements or the error-prone areas and gather all the test cases to execute for those cases. This preparation helps them gear up for the accelerated tests and also, prioritize the test cases. This way they can target the product areas that need more focus on quality. Additionally, by detecting defects early in the development cycle, regression testing can help reduce excessive rework. This helps release the product on time.
  • Facilitates Localized Changes – Regression testing makes it possible for development teams to confidently carry out localized changes to the software or sometimes, even for bigger changes. The teams mainly focus on the functionality that they planned for the sprint secure in the knowledge that the regression tests will highlight the areas that are affected by the most recent changes across the codebase.
  • Business Functionality Continuity – Since regression testing usually takes into consideration various aspects of the business functions, it can cover the entire system. The aim is to run a series of similar tests repeatedly over a period of time in which the results should remain stable. For each sprint, this helps test new functionality and it makes sure that the entire system continues to work in an integrated manner and the business functionality continues in the long run.
  • Errors Are Reduced to a Large Extent – The thing with an Agile development environment is that there is a reduced scope for errors during the accelerated release cycles. The series of regression tests at each level of the release ensures that the product is robust and resistant to bugs. This helps in enhancing the software’s stability and improves its overall quality.
  • Offers Scope to Add Better Functionalities – Introducing new functionalities in any application can be time-consuming because there are several aspects that need to be taken into consideration. This process becomes less cumbersome with Agile development, which can boost gradual changes. Regression tests amp up the power of the methodology by giving the scope of introducing several functionalities in seamlessly.
  • Quicker Turnaround – There are multiple tools for regression testing. It’s also possible to automate significant portions of the regression testing given the repetitive nature of the tests. This offers the Agile development team faster feedback. They can achieve faster turnarounds and can accelerate releases confidently.

To Sum Up:

Regression testing is a staple while developing a well-integrated, robust software as it evolves. In the accelerated Agile environment, it helps ensure that any newly developed sprint has no adverse effect on the existing code or functionality of the business. Furthermore, a carefully considered regression testing strategy helps the Agile teams be confident that every feature in the software is in perfect condition with all the updates and fixes required. It’s the insurance policy that Agile product development teams need.

Where AI Could Fall Short In Software Testing?

We have written earlier how Artificial Intelligence can increase the efficiency and speed of software product development. Now that AI in software development is gaining acceptance, let’s look at how AI can play out in software testing- its potential as well as shortcomings.

Where AI could fall short in Software Testing

After test automation, AI-based testing looks like the obvious next step. Here’s how things have rolled out in the software testing space:

  • Traditionally, manual testing has always had a role to play, because no software is produced sans bugs. Even with all the tools available, a key part of the process is handled manually by specialized testers.
  • Over time, test automation took root. In several cases, test automation is the only feasible approach when you need to run a large number of test cases, fast and with high efficiency.
  • AI-enabled testing is making test automation smarter by using quantities of data. QA engineers can feed historical data into algorithms to increase detection rates, implement automated code reviews, and automatically generate test cases.

Let’s take an overview of what AI can do in Software Testing.

The Potential of AI in Software Testing:

​As organizations aim for continuous delivery and faster software development cycles, AI-led testing will become a more established part of quality assurance. When considering only software testing tasks, there are several tasks that quality Assurance engineers perform multiple times. Automating them can drive huge increases in productivity and efficiency.

In addition to the repetitive tasks, there are also several tasks that are similar in nature, which, if automated, will make the life of a software tester easier. And AI can help identify such fit cases for automation. For instance, the automated UI test cases that fail every time we make a change in a UI element’s name can be fixed by changing the name of an element in the test automation tool.

Artificial Intelligence has several use cases in software testing, including test case execution, test planning, automation of workflows, and maintenance of test cases when there are changes in the code.

But what are the limitations?

Why AI will not take over entire QA phases?

Even though Artificial Intelligence holds strong promise for testing, it will be hard for mere technology to completely take over.

  1. Humans need to oversee AI:
  2. Artificial Intelligence can’t (yet) function on its own without human interference. Until then, organizations need human specialists to create the AI and to oversee operational aspects that are automated with AI. In short manual testers will always be a part of the testing strategy to ensure bug-free software.

  3. AI is not as sophisticated as human logic:

    While there have been significant advancements in Artificial Intelligence, it does not beat the logic, intuitiveness, and empathy inherent in humans. AI will bring about more impactful change in the way it assists software testers to help them perform their tasks with more accuracy, precision, and efficiency. But for all tasks that need more creativity, intuitive decision making, and user-focused assessments, it may have to be human software testers who hold the fort. For a while at least!

  4. AI can’t, and never will, eliminate the need for humans in Testing:
  5. Organizations can use AI-based testing tools to cover the basics of software testing, and easily uncover defects by auto-generating test cases and executing them for desktop or mobile. However, such an approach isn’t feasible when you need to assess a complex software product with various functions and features to test. Experienced software QA engineers bring a wealth of insights to the table that goes beyond the data. They can make the decisions that must be made even when data doesn’t exist. When a new feature is being implemented, AI may struggle to find enough solid data to define the way forward. Experienced software testers may be better suited to such situations where they can make intuitive leaps based on nothing more than their judgment.

  6. Functions in Software Testing that can’t be entirely trusted to AI:
  7. AI can seamlessly help with tasks that are repetitive in nature and have been done before. But, even if we leverage AI to its full potential, there are jobs within QA that demand human assistance.

    • Documentation Review – Comprehensively learning about the ins and outs of a software system and determining the length and breadth of testing required in it is something better trusted to a human.
    • Creating Tests for Complex Scenarios – Complex test cases that span several features within a software solution may be better done by a QA tester.
    • UX Testing – User experience can be tested and assured only when a user navigates the software or application. How something looks to the users and, more importantly, how it feels to them, is a task beyond the likely capabilities of AI.

Just like automation aims at reducing manual labor by addressing monotonous tasks, AI-led QA minimizes repetitive work with added intelligence by taking it up a notch up.

This means QA engineers should keep doing what they do best. However, it will help QA testers to familiarize themselves with technologies AI to advance their career when these tools become commonplace. The truth is that AI is making a stand, but we still need diligent, creative, and expert QA engineers on our product development teams.

How Microservices Comes Together Brilliantly with DevOps?

Do you know what’s common to Amazon, Netflix, and NASA?

All three of them use DevOps.

Amazon uses it to deploy new software to production at an average of every 11.6 seconds!

Netflix uses it to deploy web images into its web-based platform. They have even automated monitoring wherein they ensure that in the event of a failure in implementing the images, the new images are rolled back, and the traffic is rerouted to the old version.

NASA, on the other hand, used it to analyze data collected from the Mars Rover Curiosity.

It’s become such that every organization that focuses on quick deployments of software and faster go-to-market uses DevOps.

How microservices comes together brilliantly with DevOps?

Statista reveals that 17% of enterprises had fully embraced DevOps in 2018 as compared to 10% in 2017.

Given the advantages, these numbers will only grow every year as companies transition from the waterfall approaches to develop fast, fail quickly, and move ahead on the principles of the agile approach.

But for DevOps to deliver to its fullest potential, companies need to move from the monolithic architecture of application development to microservices architecture.

What is Microservices Architecture?

Unlike monolithic architecture, where the entire application is developed as a single unit, Microservices structures applications as a collection of services.It enables the team to build and deliver large, complex applications within a short duration.

How can Microservices Work with DevOps?

Microservices architecture enables organizations to adopt a decentralized approach to building software. This allows the developers to break the software development process into small, independent pieces that can be managed easily. These developed pieces can communicate with each other and work seamlessly. The best part about microservices architecture is it allows you to trace bugs easily and debug them without leading to redeveloping the entire software. This is also great from the customer experience perspective as they can still use the software without any significant downtime or disruption. It’s a perfect fit for organizations that use DevOps to deploy software products.

No wonder organizations like Netflix, Amazon, and Twitter that were using a monolithic architecture have transitioned towards a microservices architecture.

Let’s look at the benefits of Combining DevOps with Microservice Architecture:-

  • Continuous Deployment: Remember the Netflix example we gave at the beginning about how Netflix reroutes the traffic to the old version of web images if they are not deployed on time? Imagine if Netflix still used monolithic architecture or the waterfall method of software deployment, do you think they would have been able to give the same kind of customer experience you witness today? Most likely, not! Microservices architecture coupled with DevOps enables continuous delivery and deployment of software, which means more software releases and better quality codes.
  • More innovations and More Motivation: Imagine working on a product for 2-3 years and then knowing it is not acceptable to the market!It becomes hard to pivot too. Often you realize that there are several bugs, the process has become unnecessarily lengthy, and you have no clue which team is working on what. Wouldn’t it lower your morale? However, those days have gone. Today, organizations have transitioned from a project to a product approach. There are smaller decentralized teams of 5-7 people that have their own set of KPIs and success metrics to achieve. This allows them to take ownership of “their” product and it gives them better clarity on the progress. It also gives them the freedom to innovate, which boosts their morale.
  • High-quality Products: With the power of continuous deployment and the freedom to experiment and innovate, organizations can continuously make incremental changes to the code leading to better quality products. It allows teams to mitigate risks by plugging the security loopholes, make changes to the product based on customer feedback, and reduce downtimes.

As you can see, using DevOps and microservices architecture together will not only boost the productivity of the team, but it will also enable them to develop a more innovative and better quality product at a faster pace. It helps product teams develop products in a granular manner rather than taking a “do it all at once” approach.

However, to embrace DevOps and microservices, you have to ensure that your teams understand the core benefits and make the most of the change.

Teams usually work in silos – the development team works independently, the testing team does its job, and so on. There is an obvious gap in communication, which leads to a delay in completing development and testing. DevOps and microservices require teams to work in tight collaboration. You will have to foster an environment where there are cross-functional teams of testers and developers communicating and working together to complete a task. This will help the teams to accelerate the process of developing, testing, and deploying their piece of work at a faster pace.

Of course, it is not easy to introduce a culture of collaboration, given that people are accustomed to working in silos. Hence, it is essential to reduce friction before starting the initiative. Once everyone shares in the vision and understands their own role in getting there, developing products with DevOps while leveraging a microservices architecture will become much easier.

Key Considerations While Shifting to Microservices Architecture

 

What Exactly is a Microservice?

Microservices is a method of structuring a software or an application as a loosely coupled service. Each service executes one function and is a component of the system that can be developed, maintained, and scaled individually. Each module supports one goal and uses a simple interface to communicate with other services.

Traditionally, software programs were built using a monolithic approach, where the entire software was built as a single unit. For eg., the entire unit would be responsible for database operations, executing business logic, handling HTTPS requests, background processing, client communication, user authentication, and so on.

Key considerations while shifting to a microservices architecture

In this structure, every tiny change in the system would require rebuilding, checking, and deploying the entire application. Change, even in a line of code, would prompt developers to make a new build of the complete system, which was time-consuming.

It became clear that the monolithic architecture was holding back innovation, scalability, agility, and independence. The microservices approach brings all this and more. Where there is a large system with intricate component management, scaling, and individual development, microservices has become the obvious choice.

However, making that move from monolithic to microservices isn’t easy. There are several considerations to factors in.

Key Considerations for Businesses Shifiting To Microservices Architecture :

  • Degree of Independence – The first thing to consider is the level of independence you want for the services in your microservices architecture. In the first approach, each service is completely independent with its own UI and database. This would be an instance of an extreme microservices architecture where services are entirely decoupled and share nothing. The difficulty arises in ensuring all datastores stay in sync and updated at all times. To rectify this, in the second approach, you can choose to share some components between the services, such as the database. This would make it easier for you to ensure data consistency and enforce data standards, regulations, and compliances within the software.
  • Technology Stack – It’s hard enough to decide a tech stack for a monolithic application. Now, imagine doing that for each service within a microservices architecture. If your services are heterogeneous, this could create an issue with standardization. Moreover, it becomes harder for your people to move between teams if every team uses a different tech stack. A recommended approach is to take a balanced approach in deciding the desirable tech stack across the application. If a team wants to override the default choice, they will have to support their decision with the pros and cons of the change that compelled their decision. The ideal tech stack may include the cloud provider, infrastructure, storage, monitoring, programming language, and a testing and logging framework.
  • Complexity – Microservices impacts the operational complexity of the underlying application. You need to consider aspects such as the infrastructure which needs to be scaled up and down for a sophisticated level of automation. Load balancing and scaling, which will happen either for all services or only a few, will be a concern. Service discovery as services in a microservices world changes dynamically due to upgrades and scaling and that should be part of the consideration set. As should be monitoring, which needs to be configured for each service individually. And, think of the capability to handle scenarios when a subset of services are scaled up or down.
  • Decouple Capabilities – There is a definite cost associated with moving to microservices from monolithic systems. Therefore, you’d want to consider what capability your systems have that can be decoupled and how you can migrate incrementally. Assess this in your system by taking a look at the operational readiness for creating services or migrating them. The primary idea is to start with capabilities that are fairly decoupled right in the monolith. These could be services that don’t require changes to several client-facing applications that use monolith and don’t use a data store. After decoupling simple edge services, consider those deeply embedded in the monolithic architecture.
  • Continuous Delivery – As Martin Fowler points out in his article on microservices tradeoffs, easily deploying small independent units is a blessing for development, but operations get more complicated as a few applications become hundreds of microservices. This reinforces the vital role of continuous delivery. While CD is a valuable skill for monoliths, it’s absolutely essential for microservices. Organizations such as Netflix and Amazon have spent their energy in building homegrown custom continuous delivery pipelines for microservices. As an alternative, organizations can choose a CD automation platform for a less intensive choice.
  • Data Services – Refactoring the underlying data structures is one of the most complex issues of migrating to microservices. There are several models that you can follow. Use reference data to populate drop-downs in GUIs, Master Data Management to eliminate several views of an entity such as the customer within a database, flat object structure to store documents such as feedback surveys, independent tables to support data retrieval with SQL, and blob storage for storing a structured Java object, for instance.
  • Team Organization – Lastly, you must reorganize your teams to ensure that all services are developed, deployed, and maintained independently. You may need an independent team to work on each microservice because when engineers work on multiple microservices, they might make optimization decisions that are not in the best interest of all associated services. Each team may need to have capabilities such as development, testing, Ops, database administration, UX, and product management. The central idea is to organize teams for maximum optimization of each microservice, without dependence on other teams.

Challenges of Shifting to Microservices and Why you need a Reliable Partner:

As you would have inferred, migrating from a monolithic architecture to microservices isn’t a job for everyone. When you need to make so many key considerations and weigh several pros and cons, you need specialized assistance from a reliable partner.

At ThinkSys, we make microservices possible even when you have a hyper-complex monolithic application architecture. We can ease the many hiccups you might encounter in that shift. 

Talk to Our Team Today

What you Must know about Low and No-code Software Development?

Back in 2011, Marc Andreesen said, “Software is eating the word”. These words have become even more relevant as digitization becomes an organizational priority for enterprises across sectors. The world is becoming software-driven and for the IT department, this has translated into increased demand for software to address ever-evolving requirements.

Users have high expectations of usability and demand greater flexibility in business operations. Frequent updates and upgrades are our new normal. Version 2.0 of a product is expected to be built almost simultaneously with the first version. Business applications need amendments and scalability according to the changing needs of business users. The task is huge. And the growing shortage of seasoned developers isn’t helping the situation.

low code and no code software development

Organizations are looking at ways to make software development faster, increase automation, and also make development easier to replicate, even by business users. They are looking at doing away with repetitive coding (something that weighs down traditional software development).

And this is how we came to know and love low-code and no-code software development.

The ABC of Low Code Software Development:

Low code software development is an accelerating trend. Research suggests the area growing from USD 4.32 Billion in 2017 to USD 27.23 Billion by 2022, at a CAGR of 44.49% during the forecast period. So, what is low-code development and what makes it so attractive?

Low code development is a methodology where manual processes are automated by employing a visual IDE environment and without hand-coding. The visual IDE environment connects to the backends and also to the application lifestyle management system. Low code provides avenues for developing custom code to deploy features that are not readily available.

Low code development is great for specific business processes especially those that need integrations with other applications, systems, and databases. Apart from giving developers the benefit of developing applications faster, low code development can also be done by the non-developers (mainly the power users) in the development team. These non-developers are usually not proficient developers but have some basic coding and scripting skills.

Low code development platforms such as Salesforce Lightning Platform, The FileMaker Platform, OutSystems, KissFlow, and Zoho Creator etc.  allow developers to arrange application components including the application data and logic using a drag-and-drop interface. It is almost akin to building Lego models using virtual blocks- the developer moves the Lego block with a mouse and snaps it into their model.

But does this mean low code is right for every application? Well, not just yet. However, low code development is great for applications that are built for an express purpose as it not only accelerates the workflow but also focuses it.

What Advantages does Low-Code bring to the table?

If you are thinking of low-code development get ready to experience a slew of benefits.

  • Faster and more democratized application development including greater inputs from power-users
  • Reduced need for software developers in specific functions can lower costs
  • Greater agility for organizations to match market demands
    • Better risk management and application governance as low-code facilitates immediate change
  • Reduced complexity in app development that enables faster transformation in a digital world

The ABC of No-code Development:

Chris Wanstrath, CEO at GitHub says, “The future of coding is no coding at all”. But can this even be possible? Yes, it could with no-code development.

No-code development employs a visual development environment that helps anyone create applications. This platform uses a drag-and-drop method to add application components needed to create an application. The users need absolutely no coding knowledge. In fact,this could be anyone with an idea of building an application.

No-code development gives non-technical business users the ability to build full-fledged, complex applications that are powerful, secure, and, also, enterprise-grade. Surprised?

Such a platform uses a user interface builder, allows visual modeling to process and manage data easily and also allows for easy integrations. Essentially, a true no-code platform can be described as software that writes software. It has easy to use features such as drag and drop modules, picklist selection boxes, spreadsheet imports, etc. Tools such as Nintex, and Quick Base, and even options from Kissflow and Zoho Creator have been successfully giving users more freedom to develop applications with ease.

No code development is usually driven by specific use cases especially those that don’t need connections to third-party systems and are great for reporting analytics or tracking applications. In case such a connection is needed, it can be enabled quite easily as well.

So, What are the Benefits of No-Code?

Much like low-code, no-code development also gives us a host of benefits. It is hardly a surprise to see Forrester predicting that the no-code development market will grow from $3.8 billion in 2017 to $21.2 billion in 2022.

  • Greater agility as development happens visually using pre-built modules. Since the development time is reduced and testing is automated, organizations can build applications faster
  • Reduced overhead costs that come with having a team of highly skilled developers
  • Enabled re-usability as small parts of an application can be used in other applications
  • Enable change easily as all you need is new logic to implement change
  • Allows non-programmers to create applications at speed to fulfill business needs

Of course, the applicability is still limited and in truth platforms like low and no-code are just pushing the heavy lifting to platforms that do the hard work behind the scenes. But, that said, the value is clear.

No code and low code software development could revolutionize software development on getting established.Projects that used to take years and months can now be completed in days. The democratization of the development process also makes this methodology relevant for these times when collaboration has to be at speed to deploy new capabilities. It will be interesting to see how this plays out. Do you have a low-code or a no-code strategy in place?

Software Development Enters The AI Age

The global AI (Artificial Intelligence) software market is set to explode. The numbers may increase from around 9.5 billion U.S. dollars in 2018 to a whopping 118.6 billion by 2025. In fact, many of the top names in the tech industry are investing in AI-related R and D in a bid to embrace futuristic solutions.

Software Development Enters The AI-age

With AI penetrating almost every space possible, how can software development be left behind? Several firms are looking to accelerate software development and testing functions by using the combined power of AI and ML (Machine Learning).

Here are some of the ways in which Software Development is undergoing a Massive Transformation in the AI-age:

  1. Coding Assistance – Clean code is the way to create stable software that is easy to maintain in the long-term. Both AI and ML make it possible to analyze the code and also, optimize it for better interpretation and performance. A case in point is that of AI-powered tools, which act as coding assistants. These tools come preloaded with learning culled from several thousands of coding rules and help developers fix their code. This cuts down the time for coding, brings to the table the most relevant coding instances, and helps developers. As an added benefit, developers can focus on more creative and intuitive tasks that drive innovation, rather than on repetitive or routine tasks.
  2. Rapid Prototyping – More often than not, coming up with a software product needs tremendous amounts of planning before putting thoughts into action. AI and ML can cut short that duration by offering rapid prototyping capabilities. Think in terms of automated decision-making, optimized development process, early technical validation of ideas and so on. Ergo, developers can easily develop new applications in a shorter time, improve applications quickly and also, deliver enhanced customer experiences.
  3. Bug Fixes – One of the key pillars of the process of software development is bug fixes. AI can completely transform this step. It can detect bugs using intelligent algorithms and without the need for any manual intervention. AI can identify high-risk areas of the code based on historical data or coding patterns. A focus on those areas can help find bugs faster. This also helps reduce the possibilities of bugs being overlooked or ignored by testers under time-pressure.
  4. Software Testing – Like bug fixing, with AI at the forefront, the days of manual testing may be transformed before long. AI can help easily track the common errors and flag them early in the development. AI can also help analyze the system logs to flag down errors and, in the future, the same could be used to make dynamic changes without any human assistance. Then there is the potential impact on test automation. AI can analyze the massive volume of test cases and define which are the best cases for automation based on their priority and “automatability”. It’s only a short stretch from there to AI being able to assemble the scripts needed to automate those test cases on the fly. As the product evolves, AI can also determine how the test cases, and hence the scripts, need to be changed. In essence, AI can automate the creation of test automation!
  5. Product Maintenance – From startups to corporates, a large part of the software development costs is spent on product maintenance. Oftentimes, even the redundant features of a software product are subjected to maintenance. This is costly and time-consuming. In the AI-age, it will be easy to identify such redundant features by scanning massive chunks of data. With intelligent automation, the process will be less complicated and less prone to errors as developers will not need to manually correlate the data from multiple sources. Any unrequired features and functionalities in the software and the associated code can be easily detected and removed if need be.
  6. Bridging the Skills Gap – There’s a lot of hue and cry over AI taking over manual jobs and making certain roles redundant. But then, on the bright side, AI tools and technique can also help speed up the development process by making it easier for developers to develop products using more automated means. They won’t have to learn a new set of skills (at least not immediately). They could use ready components that can put together with little or no coding with AI doing much of the heavy lifting in the background.

For instance, consider Bayou. Bayou generates code snippets for accessing APIs in Java. All the users need to do is “Ask Bayou” for what kind of program would help them address specific tasks in their programming. Bayou then, analyzes the code from the user and the query and delivers the right API idiom. This innovative application of Neural Networks was jointly developed by Rice University and DARPA. Bayou, and others like it, represent the vanguard of the “AI coders” movement that could simplify simple software development tasks.

It seems abundantly clear that AI will become a fundamental business practice offering real business advantage to various industries, including software development. The way we develop software may be set to get more automated, less intuition-driven, and more AI-led.

What is your take on the AI-advantage for software development?

Application Development with Microservices in the DevOps Age

Does anyone even remember when companies developed an entire product, tested it, fixed it, and then shipped it? The entire process would take months, even years, before a functioning product made it to the customer. Before the product hit the market, neither did the potential customers know what it held for them and neither did the product owners know if it would hit or miss the mark.

Today, product users expect to be a part of the development process. They want to contribute their insights to develop a product that matches their ongoing needs. The need is for continuous innovation and improvements. The need is for DevOps!

DevOps combines technology and cultural philosophies to deliver products and services quickly. It is a continuous process of developing, testing, deploying, failing, and fixing applications to achieve market-fit. Jez Humble, one of the leading voices of DevOps sums it up “DevOps is not a goal, but a never-ending process of continual improvement.”

microservices Application development in Devops age

Today, DevOps is not just for a handful of large enterprises. According to Statista, the number of companies adopting DevOps went up by 17% in 2018

A quick look at what has made DevOps popular?

Apart from the continuous innovations and improvements, DevOps also helps in:

  • Improving customer satisfaction: With a DevOps mindset, companies use advanced methods to identify issues and fix them real-time before the customer is impacted. There is also scope to improve the product on-the-go driven by frequent suggestions and feedback from customers. Continuous improvement in quality leads to customer delight. Take Rabobank of Netherlands, for example. This large financial institution has over 60,000 employees and hundreds of customer-facing applications. As the deployments were manual, the failure rate was over 20%, and they received many complaints about delays. When they moved to DevOps, they were able to deploy applications 30x more frequently with a lead time that was 8,000 times faster than their peers.
  • Change in organizational culture: DevOps has played a significant role in breaking silos and boosting the collaborative culture in companies. In an agile environment, working in silos can slow down the process of developing, testing, and releasing the product. A DevOps team will be able to collaborate better and ramp up the process of developing, testing, and troubleshooting the product. 
  • A decrease in failure rates: According to the State of DevOps report, high-performing DevOps organizations have seen a reduction of failure rates of 3x, thanks to their ability to find and fix errors early in the cycle.
  • Higher productivity: DevOps organizations can deploy products 200x more frequently than a non-DevOps organization, leading to happier and highly motivated teams. Take Microsoft’s Bing, for example. It has moved developers to a DevOps environment with the idea of continuous delivery and innovation deeply ingrained within their processes. The result? Bing deploys thousands of services 20 times a week and pushes out 4000 individual changes every week. The continuous effort by the team to deliver has made Bing the second largest search engine in the world.

While adopting a DevOps culture is essential for a company to thrive, it is also crucial that they have the right architecture and systems in place to complement their principle of continuous delivery and innovation. That’s where microservices is now playing a massive role.

Micro-services and Their Role in DevOps Organization:

For a long time, companies relied on a monolithic architecture to build their application. As monolithic applications are built as a single unit, even a small change in a single element made it necessary to build a completely new version of the application. 

With more and more companies moving towards DevOps, such a monolithic architecture makes it difficult to implement changes rapidly. The need for greater agility gave rise to a new type of architecture -enter microservices. 

With Microservices, an application is built on small, independent components that are independently deployable. Although independent, these components communicate with each other via RESTful APIs. So, even if a single piece of code has to be changed in a single element, the developer does not have to build a new version of the whole product. They can simply make the changes to the individual components without affecting the entire application, making the deployment efficient and faster. 

For companies that have adopted the DevOps culture, developing applications with microservices has several benefits that include:

  • Easy rectification of errors: When a component fails the test or requires changes, it is easy to isolate and fix. This makes it easier for companies to fix errors quickly without affecting the users of other services.
  • Better collaboration: Unlike a monolithic architecture where the different teams focus only on specific functions such as UX, UI, server, etc, a microservices architecture encourages a cross-functional way of working. 
  • Decentralized governance: Monolithic architecture uses a centralized database, while microservices use a decentralized method of governance, wherein each service manages its database. This makes it easier for developers to produce tools that also can be used by others to solve specific issues.

A key trend accelerating the adoption of Microservices in such scenarios is Containerization. Containerization allows code for specific elements to be carved out, packaged with all the relevant dependencies, and then run on any infrastructure. These applications can be deployed faster and can be made secure. The applications are extremely portable and adaptable to run on different environments. 

Companies like Amazon and Netflix have shifted to microservices to scale their business and improve customer satisfaction. 

Product companies aiming to become customer-centric and delight with continuous improvement in the product may find it essential to adopt a DevOps mindset married to a transition to the microservices architecture

Of course, it will take some time to transition product development. Teething problems are bound to arise, including duplication of efforts due to the distributed deployment system. However, given the larger picture and the potential benefits, it’s a wise move for product companies to make. 

AngularJS – Making Mobile App Development Better

Google introduced AngularJS as an open-source framework specifically to address the challenges faced by the mobile app developer in creating and testing code. So, how does it do that?

AngularJS integrates HTML code along with the application modules.This forms an innovative framework that is now widely used to restructure JavaScript code. AngularJS is designed to help accumulate data by adding and eliminating extra source code to maintain the overall code integrity.

What is AngularJS?

In 2009, Google launched AngularJS, and it turned out to be a great framework for building iPhone and Android mobile apps. It doesn’t offer many add-ons but there are an array of features which makes it a perfect fit for the mobile app developer’s toolbox.

Its interactive abilities with backend, web servers and external data sources help mobile app developers build a feature-rich mobile app easily, quickly, and flawlessly.

angularjs mobile app development

A key value is that AngularJS helps to manipulate jQuery Document Object Model libraries. Another great feature of AngularJS is that it enables developers to expand the functionality of HTML by adding constructs through its two-way data binding. With its directives, mobile app developers can accumulate data into HMTL and can abstract DOM modifications.

A Summary of AngularJS:

AngularJS can be easily added to an HTML page through a simple coding script. The modular approach helps the mobile app developers segment codes, simplify testing, and update maintenance activities on different types of mobile apps. It has a few manipulations of JS and HTML, which adhere to the fundamentals, thus helping to deliver better stability.

It extends HTML templates, enabling an advanced approach in managing and editing the components. Mobile app development with AngularJS is extremely efficient, bug-free, and scalable.

Now that we are familiar with the basics of AngularJS; let’s explore how AngularJS can be very helpful in creating the best mobile apps with rich functionalities.

Build Mobile Apps with Advanced Development Practices:

Nowadays, every app is data-driven, and it makes obvious sense to use a tool that is built to address just this scenario. Simplifying the following three advanced software practices has made AngularJS the ideal choice here.

  1. Three-way Data Binding:

Data binding has become a crucial aspect of mobile app development. When the data model updates, the UI (User Interface) must also change and update. And when app UIs updates, the mobile app developer updates the mobile to match with application’s UI. This is called two-way data binding. AngularJS takes that a step further.

Three-way data binding is an idea that the developers can not only keep the UI but can also sync the data with the backend services. This means the developers can take inputs from an app, process it, save it on the webserver, and keep the UI in sync. With jQuery iOS and Android Background, the developers find AngularJS’s three-way data binding a considerable time-saver for app development.

  1. Reusable Codes:

It helps developers keep the UI, data, and code logic discrete. That means that AngularJS enables them to reuse older code on different devices and also on different platforms. It also allows the developers to customize their UI for each platform to provide a better user experience. In the past, application development was only limited to the PC and desktop.

But now, there are mobile devices with different operating systems, different functions, and different UIs. Thus, developers now have to create code for all the platforms separately. They cannot reuse that code. AngularJS addresses this issue by enabling the developers to reuse code which has been previously created.

  1. Testability:

When it comes to end-to-end unit testing, AngularJS provide advanced support. It offers flexibility to the developers so that they can conduct testing more efficiently. It has impressive testing compatibilities like the dependency injection concept to reuse, maintain, and test the code. In dependency injection, the components are given specific dependencies.
This helps to locate the code and makes it configurable. Thus, the quality analyst can quickly check the code and its dependencies and find bugs easily.

More Reasons to Use AngularJS:

Apart from three-way data binding, code reusability, and easy testing, there are other benefits that developers can experience while working with AngularJS.

  • Easy to understand: The code written in AngularJS is not only easy to understand but also easy to maintain and test.
  • Customization: While writing code, the mobile app developers do not require to add all the libraries at the same time while adding other modifications.
  • Wider Community: The Google community supports AngularJS which means the developers will get wider support to fix any issue.
  • Pre-defined Solutions: AngularJS comes with a pre-defined and versatile solution which can be used within the app. Developers can get pre-defined UI routing approaches and module practices to help them get their app created sooner.

Most Successful AngularJS Domains:

Businesses are looking to stand apart from the competition by creating impressive AngularJS mobile apps. Many tech-giants have significantly invested in AngularJS to provide a high-quality user interface and user experience. YouTube, Netflix, Upwork, and PayPal are some of the examples of mobile apps working on AngularJS. Developers looking to create mobile apps with great user experience are using AngularJS in domains like.

  • On-demand video streaming apps.
  • Travel and destination finder apps.
  • Weather updates apps.
  • User-generated and content portals.
  • Apps for user reviews.
  • Interactive social apps.

Clearly, AngularJS provides many benefits when it comes to creating seamless mobile apps. It is a futuristic framework which will continue to redefine the way mobile apps are developed. Does your mobile app strategy include AngularJS?

How Offshore Development has Changed With DevOps?

Offshore software development has never been easy. Neither has DevOps. Although both offer a distinct set of advantages to organizations, trying to do them together could be challenging. In addition to creating a culture of collaboration, new tools have to be adopted. Yet, many large global organizations have successfully built DevOps capabilities across time zones, while meeting requirements 24×7 – within time and budget. 

How offshore development has changed with DevOps?

Here’s how Offshore Development has Changed with DevOps:

  1. The Improvement in Product Quality: Quality management has always been a basic requirement of software development, and also a popular way to control development costs. But with offshore development, quality management gained a reputation for being rigid and imbalanced. Offshore teams had a tough time balancing quality and costs. The perception grew that they could only focus on one aspect while overlooking the other. However, DevOps brings in a way for offshore development teams to drive quality and costs simultaneously. Since there is more collaboration between teams, bugs are identified quickly – which improves quality, and there is less rework – which reduces the associated costs. 
  2. The Stress on Culture: Offshore development teams have often focused on the tools and technologies needed to drive outcomes. However, with the advent of DevOps, there is a ton of business culture aspects to consider. When DevOps comes into the picture, it’s not just about tooling; teams have to work together and collaborate to drive the intended DevOps outcomes. Rather than looking at culture as a nice-to-have feature, offshore development teams have started to look at it as a core competency that lays the foundation of an efficient software development practice. 
  3. Accelerated time-to-Market: Since the dawn of offshore development, teams have been following the sun; once early analysis and design are complete, documentation is sent to remote developers to start coding and testing. However, what DevOps does, is turn all of this on its head; by seeking greater collaboration between teams, it helps them release software in bite-sized sprints – so teams can get more frequent visibility and feedback. Such an approach builds faster feedback loops, accelerates the velocity at which a company can test hypotheses about what the client wants – without wasted time and effort – and brings products to market sooner. 
  4. The Elimination of Hand-offs: Offshore development has also always been about hand-offs. When one person (or team) is done with a piece of work, a key milestone is achieved, and he/she then notifies the other to start working. However, what DevOps does is just the exact opposite. It enables different teams to work on aspects of software development in tandem, while greatly reducing the number of handoffs or delays. Teams do not have to waste time waiting for a “go-ahead” to start working; instead, they drive continuous collaboration through the entire development life cycle, keep track of tasks across coding, unit testing, build scripts, configuration scripts and avoid passing work back and forth. 
  5. The Growth of Analytical Dashboards: For offshore teams having a tough time getting visibility into project status, DevOps drives the use of analytical dashboards. These dashboards often serve the purpose of providing a single source of truth across the complete organization, while giving real-time updates on project status, issues, challenges, and improvement opportunities. Teams that leverage these tools find themselves resolving issues faster while making the entire process of offshore development far more effective.
  6. Handling out-of-Scope Requests: Offshore teams have always found it difficult to handle out-of-scope requests and cater to emergency patch-up works which come out of their schedule. This is mainly due to the differences in time zone. However, with DevOps, the project’s scope is clearly defined through several iterations of communication between the internal team and the offshore team. Any out-of-scope request can be accommodated, based on the availability of resources, as can urgent jobs which need immediate attention.  

Improve Software Development Outcomes: 

When the world embraced the offshore development model, the productivity gains and cost savings stimulated technological innovation for years to come. While offshoring helped businesses achieve their market and customer goals – quickly and more efficiently, it also paved the way for the adoption of methodologies and approaches to produce software more efficiently and effectively. 

DevOps is one such transformation, that is helping offshore teams break departmental siloes, and drive a cultural shift towards efficient software delivery. The changes range from dramatically improving software quality to accelerating time-to-market, eliminating wasteful hand-offs, to offering real-time visibility into product status while seamlessly handling out-of-scope requests. The impact of DevOps on offshoring has been phenomenal, and the approach will continue to boost offshore development outcomes for years to come.

How to Prepare for Enterprise AI Adoption?

The probability is pretty high that you may have been already heard of probable use cases of AI and the potential it holds for the enterprises. According to Gartner AI adoption has increased by 300 % in 2018 alone. The giants like Google are pledging millions of dollars to tackle the AI challenge. It seems clear now that AI can provide robust ROI.

It is due to this acceptance that AI is slated to grow by 270 % more in the coming years.

enterprise AI adoption

Enterprise AI Adoption Guidelines:

In fact, AI could transform how businesses operate. But for this complete makeover to be realized, the enterprise has to adhere to certain basic guiding principles to reap the full benefits.

  1. The first step is to get a clear understanding of what is AI?AI is much larger than the chatbots all of us have experienced on the phone and home devices. Business decision-makers must acquire a clear sense of the potential of AI to come up with a relevant use case that is right for their business.
    AI is an umbrella term which covers, data analysis, collection, applying machine learning or deep learning, and coming up with relevant applications. Once the stakeholders can see the lay of the land it will be easier for them to come up with a use case that can be aligned with their vision.
  2. An important next step would be to carry out an extensive competitor analysis. Every industry has multiple segments and a variety of players. These competitors maybe at different levels of maturity with regards to AI adoption. Analyzing the moves the competition is making will help you understand the possible areas you could focus on.

These two steps will help you zero in on the likely use cases for AI. This will depend upon your specific needs and on the maturity of the organization. Choosing the right use case is of primary importance as that will have a massive impact on the ROI. AI use cases will vary according to the industry.

For the automobile industry, it might be predictive maintenance whereas for an e-commerce company a recommendation engine might be the need of the hour. Similarly, a chatbot guiding the customer through self-service options might be another potential application.

That done, mandatory due diligence or a current state assessment must follow.  This assessment will identify the current state of the data, how it is being used, the gaps, the best possible ways to go ahead and design the collection, management, and how to effectively and economically achieve the analysis of data. This assessment will also help you decide the tools and technologies that can be used to make the organization AI-ready.

This is an important stage in driving the strategy. Based on the gaps and the requirements that emerge, the company must analyze whether they have the capability in the house or will have to onboard an outside partner with the required skill set necessary for creating the AI. Obviously, this step has tremendous implications on budgets and business strategy. Selecting the right partner becomes an important decision point in the AI journey.

Once done with the make or buy decision and after having on-boarded the right partner, the groundwork must begin. The primary task at this stage is to build a scalable data foundation, whether on-premise or in the cloud. The data collation pipeline has to be set up. Tools and technologies have got to be aligned. A robust data foundation is a mandatory first step for setting forth on the AI/ML journey.

It is great to think big but start small. This means that first carrying out a small POC to see whether you get the desired result and whether the system is effective or not. Look for limited applications at this stage. For eg. apply the recommendation engine for a particular category rather than going full throttle.

Similarly, monitor one aspect of the engine health of an automobile, and see the system’s efficacy in spotting anomalies. Once the kinks are ironed out over the course of the limited scope pilot, the AI implementation can spread to different areas.

It’s clear that that the effectiveness of an AI / ML system improves as more and more data is fed into the system. Interestingly, this effectiveness does not hit a plateau but improves exponentially as it gets new data to train on. This suggests that it is imperative to ensure the system undergoes continuous improvements and updates.

Last but not least, think about the organization. Most organizations are reluctant to change. Hence it becomes important to drive AI adoption as a top-down approach. The push from the top management and their committed involvement becomes mandatory.

This is a critical aspect as it is this commitment that will convince the rest of the workforce to align with a new digital culture powered by AI.

What the Coming of 5G Means for Mobile App Development?

Can you imagine the possibilities of downloading HD movies on your phone in seconds rather than hours or minutes? What about immersive games that blur the lines between reality and alternate reality? Both the scenarios may become commonplace in the future with the coming of the next-gen cellular technology – 5G.

As is apparent, 5G is generating a lot of interest in the mobile industry. With consumer interest high, many firms are thinking of making apps that can leverage the latest tech. The relatively new tech remains a concept for the wider world, but it clearly has the potential to deliver a massive impact. The technology could also drive big changes in mobile app development.

What the Coming of 5G Means for Mobile App Development?

In no particular order, the considerations we will have to factor into the new 5G-capable apps we develop will include:

  • Exponentially High Data Speeds – 5G networks can achieve significantly faster data speeds of up to 10 Gbps. This is a stark contrast to the 1 Gbps speed offered by its predecessor 4G. That will allow for the transfer of much much higher volumes of data.
  • Low to Zero Latency – Latency is the time taken by a device to send a packet of data to a different device. On current mobile networks, this is about 50 milliseconds. With 5G, this will be reduced to about 1 millisecond. Surely, this will pave the way for several AR and VR based apps in particular.
  • Better Connection Density – IoT is going to grow over the next few years. To accommodate billions of devices that are a part of IoT, there is a need for a network that offers better connection density. 5G will be able to support the connectivity of up to 1 million devices in a span of several miles. Watch out for the new IoT-everywhere world. And for the mobile apps that will inevitably follow.
  • Precision – 5G has better precision capabilities and it will be useful for creating high-precision mobile apps, such as the ones that are GPS-enabled. Looks like location-based services may become much more common.
  • Lower Battery Consumption – Features such as reduced latency and increased speed translates to lower battery consumption. In fact, 5G will be able to extend the battery life of both IoT and mobile devices up to 10 times! This is great news for both app users and developers!
  • Opens Up New Avenues – HD content, 360-degree videos, holographic videos, multi-person video calls will all become a breeze on 5G networks. This will help developers create a variety of apps that cater to the users’ interests.

How 5G Will Change Mobile App Development?

We have seen a picture of the future of 5G and how it is going to shape mobile app development. Here is what is 5G mobile app developers need to think about:

  1. They Can Develop Apps with Rapid File Transfers – Several apps, especially productivity apps require the users to exchange files, data, and other forms of information. 5G with low latency and high speed will be a boon for such apps and offer the users speedy transfer. Think how smoothly your apps will run without any lag! This also means that users will be highly satisfied with the overall experience and won’t abandon the app/uninstall it from their device.
  2. Media-Rich UX – 5G will be able to provide rich UX like never before. From watching movies to listening to music, or playing a high-quality game, the user experience will be uber-intense on the new network. This means that developers can give free rein to their imagination when it comes to the UI and take it to the next level.
  3. Better Capacities and Features – For IoT, AR/VR, and other apps that use new technologies, developers will be able to build feature-rich apps that have exceptional user experiences. Powerful apps with outstanding user experiences will drive new monetization possibilities and increase app revenues as well.
  4. More Navigational Apps – 5G will be useful for creating GPS-enabled apps, which means that there will be more navigational apps in the future. The network will offer high-quality and uninterrupted communication, which is going to be a boon for such apps that are useful for the travel and tourism genre of apps as well as for utility apps or apps for wearables.
  5. Swift Feedback – Chatbots and voice bots powered by 5G networks will be able to provide instant feedback and offer better, real-time experiences to the users. No more wait times mean better conversations. This promises many use-cases like specialized apps for the customer care sector.
  6. Low Dependence on Hardware – The enhanced communication speed and delivery speed of 5G network also mean that the mobile apps of the future will be less dependent on device hardware. Therefore, it may well pave the way for device-agnostic apps.

The Road Ahead

While the coming of the 5G network will bring forth a plethora of opportunity for mobile apps, the path forward will be challenging. 5G, like any technology, will come with its own set of challenges. Evolving standards, security loopholes, setup of new business models, and the creation of the necessary infrastructure are some of the core issues at the nascent stage of adoption. This is why, even though the technology will be a game-changer, mobile-app developers need to tread carefully.

That said, there are opportunities aplenty to leverage 5G’s capabilities to design and deliver cutting-edge apps even in the early days of 5G implementation. It’s time to break out of the rut of slow, high-latency, low-capacity networks and embrace the promise of 5G!

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?