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Coverage rewarded: Test input generation via adaptation-based programming

TitleCoverage rewarded: Test input generation via adaptation-based programming
Publication TypeConference Paper
Year of Publication2011
AuthorsGroce, A.
Conference NameIEEE/ACM International Conference on Automated Software Engineering
Pagination380 - 383
Date Published11/2011
Conference LocationLawrence, KS
ISBN Number978-1-4577-1638-6
Keywordsreinforcement learning, software testing

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 naïve 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.