Which Crashes Should I Fix First?: Predicting Top Crashes at an Early Stage to Prioritize Debugging Efforts
Which Crashes Should I Fix First?: Predicting Top Crashes at an Early Stage to Prioritize Debugging Efforts
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DOI：
10.1109/tse.2011.20
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发表时间：
2011-05
影响因子：
7.4
通讯作者：
Dongsun Kim;Xinming Wang;Sunghun Kim;A. Zeller;S. Cheung;S. Park
中科院分区：
文献类型：
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作者：
Dongsun Kim;Xinming Wang;Sunghun Kim;A. Zeller;S. Cheung;S. Park
许多流行的软件系统会自动向供应商报告故障，使开发人员能够专注于最紧迫的问题。但是，需要一定的时间来评估哪些故障发生得最频繁。在对Firefox和Thunderbird崩溃报告数据库的实证调查中，我们发现只有10到20个崩溃占崩溃报告的绝大多数;因此，预测这些“顶级崩溃”可以大大提高软件质量。通过训练机器学习器了解过去版本中最常见的崩溃的特征，我们可以在新版本发布之前有效地预测最常见的崩溃。这可以快速解决最重要的崩溃，从而改善用户体验并更好地分配维护工作。
Many popular software systems automatically report failures back to the vendors, allowing developers to focus on the most pressing problems. However, it takes a certain period of time to assess which failures occur most frequently. In an empirical investigation of the Firefox and Thunderbird crash report databases, we found that only 10 to 20 crashes account for the large majority of crash reports; predicting these “top crashes” thus could dramatically increase software quality. By training a machine learner on the features of top crashes of past releases, we can effectively predict the top crashes well before a new release. This allows for quick resolution of the most important crashes, leading to improved user experience and better allocation of maintenance efforts.