{"id":260280,"date":"2014-07-20T01:48:37","date_gmt":"2014-07-20T08:48:37","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=260280"},"modified":"2018-10-16T20:41:37","modified_gmt":"2018-10-17T03:41:37","slug":"crashlocator-locating-crashing-faults-based-on-crash-stacks","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/crashlocator-locating-crashing-faults-based-on-crash-stacks\/","title":{"rendered":"CrashLocator: Locating Crashing Faults Based on Crash Stacks"},"content":{"rendered":"
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.<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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