一句话：提出 Chameleon，在 concolic 测试中在线学习并自适应切换搜索启发式，以提升覆盖率和缺陷发现。
Concolic 测试通过搜索启发式选择目标路径，以缓解路径爆炸并尽快提高覆盖率。然而已有方法通常在完整测试过程中固定使用同一启发式，难以覆盖多样执行路径。本文提出 Chameleon，一种在 concolic 测试过程中自适应改变搜索启发式的方法。它利用测试期间积累的知识在线学习新的搜索启发式，并据此动态调整路径选择策略。实验结果表明，从传统非自适应方法转向该方法后，concolic 测试在代码覆盖率和缺陷发现方面的实用性均有显著提升。
We present Chameleon, a new approach for adaptively changing search heuristics during concolic testing. Search heuristics play a central role in concolic testing as they mitigate the path-explosion problem by focusing on particular program paths that are likely to increase code coverage as quickly as possible. A variety of techniques for search heuristics have been proposed over the past decade. However, existing approaches are limited in that they use the same search heuristics throughout the entire testing process, which is inherently insufficient to exercise various execution paths. Chameleon overcomes this limitation by adapting search heuristics on the fly via an algorithm that learns new search heuristics based on the knowledge accumulated during concolic testing. Experimental results show that the transition from the traditional non-adaptive approaches to ours greatly improves the practicality of concolic testing in terms of both code coverage and bug-finding.
DOI 原文 ·
@inproceedings{paperbot5063,
title = {Concolic testing with adaptively changing search heuristics},
author = {Sooyoung Cha and Hakjoo Oh},
booktitle = {FSE},
year = {2019},
doi = {10.1145/3338906.3338964}
}