5 key drivers for software testing in 2018: driving the digital change

Software testing has seen two major transitions in the past decade. The advent of agile heralded the shift from manual to automation and then in recent times, DevOps is driving the 2nd wave of change for testing teams with continuous integration and delivery. Although, in many ways, we are seeing some of the old practices coming back to life again in testing. Let us explore some major drivers that are becoming extremely important in 2018 and will remain in the future.


The DevOps paradigm and continuous delivery have led to a digital shake-up for enterprise IT. With DevOps comes a new kind of agility, compressed release cycles, enhanced application quality and above all a renewed synergy between development and operations. This breaking of barriers is essential to match the transformation journey.

Yet, it is not just speed and quality; the business value of DevOps is quite remarkable. It evolves the paradigm of continuous integration to continuous development. Simply put, the code that is written and committed to version control is now being built, developed, tested and installed in the production environment for consumption. This helps the entire ecosystem because environments and processes become standardised and every action in this chain becomes automated.

Software testing role becomes crucial here as ‘test early, test frequently’ is the key to achieving better quality software for DevOps teams. This requires the effective use of key enablers like test automation, continuous integration, capability for continuous feedback and the right mix of tools and process for DevOps.

Test automation

But with great power comes great responsibility. Testing is one of the key pieces in the continuous delivery puzzle and organisations are now scaling up their agile and DevOps services toward continuous testing and continuous delivery. There is high pressure to fix defects urgently, in order to reduce the technical debt and achieve the pace that is set by agile and DevOps thinking.

Thus, as companies shorten their sprint cycles and increase their release velocity, testing must match both the high frequency and volume needs. Manual testing is simply not enough to accomplish this pace. Test Automation is one of the key drivers for DevOps and digital transformation. greatly increases your coverage and accuracy.

Test automation is at the heart of your CI/CD pipeline to achieve the coverage and accuracy. From faster feedback loops, reduced expenses, reusability, organisation and faster time to market, automated testing vastly improves your efficiency and overall quality.

Artificial intelligence & machine learning

Modern test management has the advantage of information. The vast amounts of test data and results produced by their test automation suites hold valuable insights and intelligence. And while it is manually impossible to wade through all this data, BOTs can quickly analyse terabytes of information. BOT-enabled tools can then produce actionable insights and optimisation recommendations. These analytics are useful to detect and reduce/remove performance bottlenecks and also identify areas of most failed scenarios or tips/insights on critical areas.

Artificial intelligence, machine learning and the use of BOTs have emerged as the major disruptive forces in the current digital landscape. By leveraging the power of AI and ML in their test automation efforts, businesses can significantly increase their ability to fulfil time to market pressures while also meeting the stringent quality needed for desired business outcomes. These AI-and ML-led smarter test automation solutions – also known as intelligent testing will be the core differentiators in achieving frictionless automation and continuous feedback.

Unified testing platforms

The organic progression of software testing organisations has led to the use of various legacy tools and processes, and teams with a wide variety of scripting languages and methods. This weighs down the test automation efforts as it leads to duplicity of efforts, lack of reusability and high maintenance overheads. The cumulative outcome is increased the time to market and non-optimisation of software quality.

DevOps and digital transformation have set the pace for a highly proactive, fast and integrated delivery culture. The confidence in the quality and timeliness of releases requires a unified approach that transcends the siloes and barriers. This has led to the era of tightly integrated, unified testing platforms that address various testing needs and challenges under one umbrella. Modern, unified solutions provide a holistic test management solution that optimizes the testing cycle with end-to-end coverage and integrates with your existing automation, CI/CD tools and project management tools.

Predictive & prescriptive quality analytics

Delivering quality at the speed of business is a digital imperative. This needs businesses to define the requirements clearly, use the right infrastructure, and use established processes. AI and ML algorithms have made it possible to mine large volumes of data from automation suites and test management tools to gain insights and intelligence. Predictive analytics can help you shorten the testing cycles by optimising your process, anticipate defects early and set the priorities for testing that will have the greatest impact.

Prescriptive analytics goes one step further and makes automation smarter by providing a holistic view of root causes and failures, heat map-visualisations and providing recommendations that are both actionable and accurate.

The game changer

Delivering software quality continuously is the need of the day in the era of digital transformation. As the lines continue to blur with DevOps and agile practices enabling more fluid and transparent work practices, integration and visibility are essential for effective test management. Software test automation holds the power to increase your coverage, depth and scope and solve many of the prevalent challenges.

But it is intelligent test automation that is the real game-changer, with its power of data, AI and machine learning. Intelligent testing takes quality engineering to the next level in the DevOps context by giving you an unparalleled competitive advantage. That of optimising testing activities, instant feedback, faster data-driven decisions and self-adaptive software testing for your quality lifecycle. Smarter testing led by AI and BOTs has beckoned the third wave in the software testing ecosystem.

Written by Rutesh Shah, CEO, Infostretch Corp