论文标题
通过突变测试调查覆盖范围的模糊
Investigating Coverage Guided Fuzzing with Mutation Testing
论文作者
论文摘要
覆盖范围引导的模糊（CGF）是一种有效的测试技术，已检测到来自各种软件应用程序的数十万个错误。它着重于最大化代码覆盖范围，以揭示在模糊过程中更多的错误。但是，较高的覆盖范围并不一定意味着更好的故障检测能力。触发错误不仅涉及行使特定程序路径，还涉及到该路径中有趣的程序状态。在本文中，我们使用突变测试来改善CGF检测错误。我们使用突变分数作为反馈来指导模糊检测错误，而不仅仅是涵盖代码。为了评估我们的方法，我们对5个基准进行了精心设计的实验。我们选择最先进的模糊技术Zest作为基线，并使用我们的方法在其上构建两种改良技术。实验结果表明，我们的方法可以在代码覆盖范围和错误检测中改善CGF。
Coverage guided fuzzing (CGF) is an effective testing technique which has detected hundreds of thousands of bugs from various software applications. It focuses on maximizing code coverage to reveal more bugs during fuzzing. However, a higher coverage does not necessarily imply a better fault detection capability. Triggering a bug involves not only exercising the specific program path but also reaching interesting program states in that path. In this paper, we use mutation testing to improve CGF in detecting bugs. We use mutation scores as feedback to guide fuzzing towards detecting bugs rather than just covering code. To evaluate our approach, we conduct a well-designed experiment on 5 benchmarks. We choose the state-of-the-art fuzzing technique Zest as baseline and construct two modified techniques on it using our approach. The experimental results show that our approach can improve CGF in both code coverage and bug detection.
