Modern software systems are highly complex and often have multiple dependencies on external parts such as other processes or services. This poses new challenges and exacerbate existing challenges in different aspects of software Quality Assurance (QA) including testing, debugging and repair. The goal of this talk is to present a novel AI paradigm for software QA (AI4QA). A quality assessment AI agent uses machine-learning techniques to predict where coding errors are likely to occur. Then a test generation AI agent considers the error predictions to direct automated test generation. Then a test execution AI agent executes tests, that are passed to the root-cause analysis AI agent, which applies automatic debugging algorithms. The candidate root causes are passed to a code repair AI agent that tries to create a patch for correcting the isolated error.
Functional test generation for FSMs by fault extraction
