Perforce Software this week added an ability to use artificial intelligence (AI) in place of scripts to test mobile and web applications using its Perfecto platform.
Stephen Feloney, vice president of product management at Perforce, said the AI Validation capability visually inspects applications rather than simply creating an AI copilot to generate scripts. That approach enables the Perfecto platform to continuously adapt to changes to objects in applications without either an AI tool or a human tester having to create additional scripts, he added.
The AI Validation tool made available by Perforce employs multiple large language models (LLMs) to inspect applications, which are accessed via a natural language interface. Those LLMs are even able to verify the meaning behind dynamic elements, such as charts, dashboards, or graphics, to ensure user experiences reflect the actual intent of the content being surfaced, noted Feloney.
The overall goal is to make it simpler for application developers to launch tests as they are writing code rather than having to wait for a separate testing team to generate scripts and then run a test, said Feloney.
That’s critical because manually updating scripts and object locators as applications are updated frequently results in the test as originally constructed failing to run at all, he added.
DevOps teams will also no longer need to create and maintain a library of testing scripts, most of which are never used by developers simply because either they don’t know they exist or they lack the skills and expertise to invoke them, said Feloney.
Perforce in the months ahead intends to extend the reach and scope of the AI capabilities of its testing platform to advance autonomous testing, make it simpler to create tests using low-code tools, provide suggestions for tests, pinpoint issues using a simple dashboard and even build suites of regression tests that validate the relationship between objects in an application.
Many of the regression tests that DevOps teams run today generally fail as applications are continuously updated, noted Feloney.
It’s not clear what impact AI will ultimately have on the need for a separate team to test applications but it’s not likely that AI will eliminate the need for those roles anytime soon, said Feloney. However, AI will ultimately improve the quality of applications as it becomes simpler for application developers to not only automatically run most tests themselves, but also remediate issues using advice surfaced by an AI tool, he added.
At this point, the AI genie is not going back into the bottle. The challenge now is determining how best to imply these capabilities. There’s little doubt that AI can be used to write more code faster but if the quality of that code isn’t good enough to run in a production environment there’s not enough value to justify the investment. In fact, instead of first investing in AI to create more code, DevOps teams would be better served by prioritizing investments in AI tools that improve the quality of the code being created.