(Oregon State University, USA)
This paper introduces a new approach to test input generation, based on reinforcement learning via easy to use adaptation-based programming. In this approach, a test harness can be written with little more effort than is involved in naive random testing. The harness will simply map choices made by the adaptation-based programming (ABP) library, rather than pseudo-random numbers, into operations and parameters. Realistic experimental evaluation over three important fine-grained coverage measures (path, shape, and predicate coverage) shows that ABP-based testing is typically competitive with, and sometimes superior to, other effective methods for testing container classes, including random testing and shape-based abstraction.