Although Artificial Intelligence (AI) is nothing new, applying AI techniques to software testing started to become feasible just the past couple years. Inevitably, AI will soon become part of our day-to-day quality engineering process. But before we get caught up in the exuberance of the technology, let’s take a step back and assess how AI can help us achieve our quality objectives.
It’s been suggested that AI could be applied to actions such as prioritizing testing and automation, generating and optimizing test cases, enhancing UI testing, reducing tedious analysis tasks, and helping to determine pass/fail outcomes for complex and subjective tests. However, should AI be applied in these cases? And where else could it assist?
Read this paper to learn:
Our realistic and honest overview of what is currently feasible, what might be feasible, and what seems to be impossible to achieve any time soon
Whether these use cases require the use of rule-based systems or learning systems
About some of the AI projects that Tricentis is currently actively researching