9619787 Software testing is a critical part of software development. A new approach to software testing based on AI Planning is investigated. The similarity of plans to test cases for some systems (e.g., command language and transaction based systems) makes the representation well suited for test generation. The representation encourages testers to think at a higher level when designing tests. To exploit these potential advantages, an automated test generator is constructed with an AI planning system at its core. This generator allows representation of testing goals and testing criteria to support a variety of testing tasks. The project's contribution to Software Testing is to extend how testing goals are articulated. Its contribution to AI planning is developing new search strategies for efficient planning, and determining how to generate goals that conform to tester's intent, and evaluating the efficacy of using AI planners during software testing. Tasks include 1) improve planning efficiency for generating large tests; 2) include representation of test focus and various test criteria to make the approach flexible; 3) represent high level testing goals to reduce tedium for testers; 4) evaluate the efficacy of the new approach to identify strengths and weaknesses of using AI Planning for testing. ***
