CrashLocator: locating crashing faults based on crash stacks

Rongxin Wu, Hongyu Zhang, S. Cheung, Sunghun Kim
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引用次数: 122

Abstract

Software crash is common. When a crash occurs, software developers can receive a report upon user permission. A crash report typically includes a call stack at the time of crash. An important step of debugging a crash is to identify faulty functions, which is often a tedious and labor-intensive task. In this paper, we propose CrashLocator, a method to locate faulty functions using the crash stack information in crash reports. It deduces possible crash traces (the failing execution traces that lead to crash) by expanding the crash stack with functions in static call graph. It then calculates the suspiciousness of each function in the approximate crash traces. The functions are then ranked by their suspiciousness scores and are recommended to developers for further investigation. We evaluate our approach using real-world Mozilla crash data. The results show that our approach is effective: we can locate 50.6%, 63.7% and 67.5% of crashing faults by examining top 1, 5 and 10 functions recommended by CrashLocator, respectively. Our approach outperforms the conventional stack-only methods significantly.
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CrashLocator:基于崩溃堆栈定位崩溃故障
软件崩溃很常见。当发生崩溃时,软件开发人员可以在用户允许的情况下收到报告。崩溃报告通常包括崩溃时的调用堆栈。调试崩溃的一个重要步骤是识别有问题的函数,这通常是一项繁琐而费力的任务。在本文中,我们提出了CrashLocator,一种利用崩溃报告中的崩溃堆栈信息来定位故障函数的方法。它通过使用静态调用图中的函数扩展崩溃堆栈来推断可能的崩溃跟踪(导致崩溃的失败执行跟踪)。然后计算每个函数在近似崩溃轨迹中的可疑性。然后,这些函数根据它们的可疑度评分进行排名,并推荐给开发人员进行进一步调查。我们使用真实的Mozilla崩溃数据来评估我们的方法。结果表明,我们的方法是有效的:通过检查CrashLocator推荐的前1、5和10个功能,我们可以分别定位50.6%、63.7%和67.5%的崩溃故障。我们的方法明显优于传统的仅堆栈方法。
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ISSTA '22: 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, South Korea, July 18 - 22, 2022 ISSTA '21: 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, Denmark, July 11-17, 2021 Automatic support for the identification of infeasible testing requirements Program-aware fuzzing for MQTT applications ISSTA '20: 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, USA, July 18-22, 2020
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