Artificial intelligence (AI) is starting to be applied to the writing and update test scripts. Perfecto has unfurled Perfecto Codeless, which employs machine learning algorithms to automate writing of test scripts in a way that allows them to not only run continuously, but also adjust to changes made to the application.
Eran Kinsbruner, chief evangelist for Perfecto, a unit of Perforce, said writing test scripts is still largely a manual process, which means as the number of applications being developed continues to escalate, the testing process becomes highly constrained.
To address that issue, Perfecto has created a codeless approach delivered via a cloud service as part of the company’s Smart Automation platform. If any code within an application gets deleted, moved or changed, the changes will automatically be reflected in the test script. That capability eliminates the need for DevOps teams to wait for a developer to update the test script.
Exactly who in an organization is writing those test scripts varies: Kinsbruner noted that many developers now prefer to write their own test scripts during the development process before their code is reviewed by a peer. That approach eliminates many of the more routine errors, enabling more time to be spent on optimizing code. Other organizations employ dedicated testers who spend most of their time writing test scripts. But many of those organizations are finding that testers cannot keep pace with the current rate at which applications are being developed in the DevOps era.
There’s no doubt machine learning algorithms are about to play a much bigger role in application testing and DevOps at large. AI is not going to replace DevOps teams anytime soon, but many mundane functions such as the writing of test scripts can be automated using machine learning algorithms. Arguably, testing is one of the areas that lends itself most to emerging AI-based platforms and services. After all, if testing becomes a bottleneck, the chances that applications with issues are deployed in a production environment increase substantially. Many of those issues can be fixed easily, but many times at the risk of annoying end users who increasingly are unforgiving when it comes to application experiences. It’s not uncommon for users to discontinue using a mobile app if the first experience isn’t immediately gratifying.
Of course, many DevOps teams already view machine learning algorithms with a certain amount of skepticism. But the rise machine learning algorithms within DevOps is now all but inevitable. IT leaders, however, should realize it may take a while for those algorithms to learn the environment. The return on investment for any AI project is not likely to be immediate because a significant amount of time needs to be invested in training the algorithms. The good news is, those algorithms never take a day off and suddenly don’t resign one day to take a better-paying job elsewhere.
