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Register a new userLegion: Best-First Concolic Testing
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Added by
Dongge Liu
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Concolic execution and fuzzing are two complementary coverage-based testing techniques. How to achieve the best of both remains an open challenge. To address this research problem, we propose and evaluate Legion. Legion re-engineers the Monte Carlo tree search (MCTS) framework from the AI literature to treat automated test generation as a problem of sequential decision-making under uncertainty. Its best-first search strategy provides a principled way to learn the most promising program states to investigate at each search iteration, based on observed rewards from previous iterations. Legion incorporates a form of directed fuzzing that we call approximate path-preserving fuzzing (APPFuzzing) to investigate program states selected by MCTS. APPFuzzing serves as the Monte Carlo simulation technique and is implemented by extending prior work on constrained sampling. We evaluate Legion against competitors on 2531 benchmarks from the coverage category of Test-Comp 2020, as well as measuring its sensitivity to hyperparameters, demonstrating its effectiveness on a wide variety of input programs.
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Software testing is one of the most popular validation techniques in the software industry. Surprisingly, we can only find a few approaches to testing in the context of logic programming. In this paper, we introduce a systematic approach for dynamic testing that combines both concrete and symbolic execution. Our approach is fully automatic and guarantees full path coverage when it terminates. We prove some basic properties of our technique and illustrate its practical usefulness through a prototype implementation.
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