Brandon N. Keller, Andrew Meneely, Benjamin S. Meyers
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摘要

背景:软件工程师必须保持警惕,防止和纠正漏洞和其他严重错误。为了满足这种需求,已经开发了许多工具和技术来帮助开发人员。Fuzzers通过自动生成测试程序的输入,承诺通过检测内存损坏、输入处理、异常情况和其他问题来节省时间。目的:这项工作的目标是通过分析OSS-Fuzz产生的错误历史,使开发人员能够优先考虑他们的质量保证。特别地,我们通过度量bug寿命、学习机会和bug类型,检查了当项目采用模糊测试作为质量保证实践时会发生什么。方法:我们分析了2022年3月12日之前由OSS-Fuzz公开的44102个报告问题。我们跟踪了由重复的模糊测试报告到源代码库的Git提交范围,以确定模糊测试错误在系统中保留了多长时间,谁修复了这些错误,以及模糊测试者在历史上发现了什么类型的问题。我们确定贡献bug的提交,以估计何时引入包含bug的代码,并度量从引入到检测到修复的时间轴。结果:我们发现在OSS-Fuzz中检测到的错误的平均寿命为324天,但是一旦检测到错误,仅在2天的中位数内保持未解决。此外,我们发现在8099个可以确定源代码提交作者的问题中,只有不到一半(45.9%)的问题是由引入错误的同一作者修复的。结论:结果表明,模糊测试可以对一个项目产生积极的影响,该项目利用了他们在一个有利于在产品发布之前修复错误的时间框架内解决错误的能力。然而,我们发现作者没有纠正自己错误的速度表明,并不是所有的开发人员都从模糊反馈提供的学习机会中受益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What Happens When We Fuzz? Investigating OSS-Fuzz Bug History
BACKGROUND: Software engineers must be vigilant in preventing and correcting vulnerabilities and other critical bugs. In servicing this need, numerous tools and techniques have been developed to assist developers. Fuzzers, by autonomously generating inputs to test programs, promise to save time by detecting memory corruption, input handling, exception cases, and other issues.AIMS: The goal of this work is to empower developers to prioritize their quality assurance by analyzing the history of bugs generated by OSS-Fuzz. Specifically, we examined what has happened when a project adopts fuzzing as a quality assurance practice by measuring bug lifespans, learning opportunities, and bug types.METHOD: We analyzed 44,102 reported issues made public by OSS-Fuzz prior to March 12, 2022. We traced the Git commit ranges reported by repeated fuzz testing to the source code repositories to identify how long fuzzing bugs remained in the system, who fixes these bugs, and what types of problems fuzzers historically have found. We identified the bug-contributing commits to estimate when the bug containing code was introduced, and measure the timeline from introduction to detection to fix.RESULTS: We found that bugs detected in OSS-Fuzz have a median lifespan of 324 days, but that bugs, once detected, only remain unaddressed for a median of 2 days. Further, we found that of the 8,099 issues for which a source committing author can be identified, less than half (45.9%) of issues were fixed by the same author that introduced the bug.CONCLUSIONS: The results show that fuzzing can be used to makes a positive impact on a project that takes advantage in terms of their ability to address bugs in a time frame conducive to fixing mistakes prior to a product release. However, the rate at which we find authors are not correcting their own errors suggests that not all developers are benefiting from the learning opportunities provided by fuzzing feedback.
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