AI and Machine Learning to Optimize Software Testing

Testing/QA in an agile environment is one of the most common and significant challenges. Automation is the only viable option. Yet, while nearly 60 percent of enterprises practice Agile, just 16 percent are also automating test activities. Many organizations are turning to BOTs, artificial intelligence, and machine learning to improve their software development agility, quality, and efficiency shares, Manish Mathuria, Co-Founder and CTO, Infostretch

In today’s digital economy speed is paramount to a successful operation. When it comes to software, Agile development is how speed-to-market is achieved, forcing enterprises to become as fleet-footed as possible to retain their market-leading positions.

However, testing/QA in an agile environment is one of the most common and significant challenges – a problem that is impacting most enterprises. The bigger they are, the more time-consuming it is to test with agility; hence, automation is the only viable option to meet business objectives.

According to the 2017-2018 World Quality ReportOpens a new window , while nearly 60% of North American and European markets practice Agile, just 16% are also automating test activities. Other studies indicate that lack of automation is hampering uptake of Agile practices, as well as digital transformation initiatives. Although our experience has shown that test automation is essential to success in today’s digitally-driven era, The World Quality Report shows that the adoption of automated testing is still relatively low. New ways of accelerating automated testing are required.

This is not a new problem, nor is test automation the new solution. What is new is the use of BOTs, artificial intelligence and machine learning to optimize mass testing activities without compromising the quality of deliverables.

While companies are clamoring for methods that will quickly and efficiently optimize the testing cycle, we have found that digital, AI, and ML processes can save at least 35% in testing efforts. The result is the rather unusual combination of development that is at once (1) faster (agility), (2) better (quality), and (3) less expensive (efficiency). Generally, enterprises are forced to choose only two of these three characteristics. It’s a software truism: “Better, faster, cheaper: choose any two.”

As suggested in the World Quality Report, testing often isn’t practical in agile environments in large enterprises. Many companies have test-case backlogs in the thousands, and so they need automation to address the issue. Artificial Intelligence and Machine Learning can weed-out human errors, reduce duplication and other problems, remove duplication and improves traceability. This approach helps to raise enterprises’ levels of test automation.

AI processes and insights help optimize testing on what is right, rather than simply testing more. AI-powered automated testing improves test case quality. And, AI-powered Quality Engineering services reduce the time, cost and scalability deficiencies of traditional testing approaches.

Put more simply, automation is great for running similar, repetitive tests; AI-powered test automation improves the process by learning patterns and predicting problems.

How is this done? A few examples of how Infostretch incorporated AI:

• A large bank to employ AI-powered automated testing to remove all dead cases from a 150,000-case repository, while reducing overhead by 30%. They use semantic comparison for analysis, optimizing their efforts by 30-40% which resulted in the updated apps working quicker… and into customer hands more quickly. The process improved their agility, the quality of the result, and the efficiency in getting there.
• A large digital health company implemented logic programming to achieve 90% test automation, applying predictive analysis to reduce multi-channel testing by 92%. This process enhanced quality prior to execution (and enabled FDA approval for their new, first-of-a-kind product launch).
• A leading payment processing company increased test automation by more than 60% to implement predictive defect detection. Through Knowledge Engineering, they improved app quality and enabled the acceleration of their innovation and vision. Cognitive computing helped them to migrate 15,000 test cases from multiple frameworks into a uniform model. This enabled the company to achieve transaction growth of 34% and gave them access to a $95B market.

Large service providers have started to include AI & ML powered solutions in their test offerings; the bad news is that many of them cater only to a specific need and do not cover the entire spectrum of the test cycle. A most effective solution will cover the full end-to-end testing cycle.

Enterprises striving to improve software quality, release software faster and scale up their activities are beginning to take test automation more seriously. For those enterprises whose aim is to digitally transform themselves, test automation is a practice that pretty much underpins all efforts in this regard. For these companies, BOTs, Artificial Intelligence and Machine Learning are increasingly driving improved results in the three areas of agility, quality, and efficiency.