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When would this bug get reported?

机译:这个错误何时报告?

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Not all bugs in software would be experienced and reported by end users right away: Some bugs manifest themselves quickly and may be reported by users a few days after they get into the code base; others manifest many months or even years later, and may only be experienced and reported by a small number of users. We refer to the period of time between the time when a bug is introduced into code and the time when it is reported by a user as bug reporting latency. Knowledge of bug reporting latencies has an implication on prioritization of bug fixing activities-bugs with low reporting latencies may be fixed earlier than those with high latencies to shift debugging resources towards bugs highly concerning users. To investigate bug reporting latencies, we analyze bugs from three Java software systems: AspectJ, Rhino, and Lucene. We extract bug reporting data from their version control repositories and bug tracking systems, identify bug locations based on bug fixes, and back-trace bug introducing time based on change histories of the buggy code. Also, we remove non-essential changes, and most importantly, recover root causes of bugs from their treatments/fixes. We then calculate the bug reporting latencies, and find that bugs have diverse reporting latencies. Based on the calculated reporting latencies and features we extract from bugs, we build classification models that can predict whether a bug would be reported early (within 30 days) or later, which may be helpful for prioritizing bug fixing activities. Our evaluation on the three software systems shows that our bug reporting latency prediction models could achieve an AUC (Area Under the Receiving Operating Characteristics Curve) of 70.869%.
机译:并非所有软件中的所有错误都会立即经验丰富,并通过最终用户报告其他几个月甚至几年的表现出来,只有少数用户才能经历并报告。我们在将BUG引入代码和用户报告的时间时,请参考时间之间的时间,作为错误报告延迟。错误报告延迟的知识对错误修复的优先顺序有所意义 - 具有低报告延迟的错误可能比具有高延迟的人更早地修复,以便将调试资源转换为高度关注用户的错误。要调查错误报告延迟,我们会分析来自三个Java软件系统的错误:AspectJ,Rhino和Lucene。我们从其版本控制存储库和错误跟踪系统中提取错误报告数据,识别基于错误修复的错误位置,以及基于错误代码的更改历史的后跟踪错误引入时间。此外,我们删除了非必要的变化,最重要的是,从他们的治疗/修复中恢复错误的根本原因。然后,我们计算错误报告延迟,并发现错误具有不同的报告延迟。基于计算出的报告延迟和功能我们从错误中提取,我们构建了可以预测错误的分类模型,这些模型是否会在早期(30天内)或更高版本中,这可能有助于优先考虑错误修复活动。我们对三个软件系统的评估表明,我们的错误报告延迟预测模型可以实现70.869%的AUC(接收操作特性曲线下的区域)。

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