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?

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.