Summarizing software artifacts: a case study of bug reports
Summarizing software artifacts: a case study of bug reports
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DOI：
10.1145/1806799.1806872
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发表时间：
2010-05
期刊：
影响因子：
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通讯作者：
Sarah Rastkar;G. Murphy;Gabriel Murray
中科院分区：
文献类型：
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作者：
Sarah Rastkar;G. Murphy;Gabriel Murray
作为软件开发项目的一部分，创建，维护和进化了许多软件工件。当软件开发人员从事一个项目时，他们会与现有的项目工件进行交互，从而进行诸如阅读以前提交的错误报告以搜索重复报告之类的活动。这些活动通常要求开发人员仔细阅读大量文本。在本文中，我们研究是否可以自动有效地总结软件文物，以便开发人员可以咨询较小的摘要，而不是整个工件。为了将重点放在调查中，我们考虑了错误报告的摘要。我们发现，现有的基于对话的发电机比随机发电机可以产生更好的结果，并且在错误报告上专门培训的发电机可以比现有基于对话的发电机更好地执行统计学上的性能。我们证明了人类还发现这些生成的摘要合理，表明摘要可以有效地用于许多任务。
Many software artifacts are created, maintained and evolved as part of a software development project. As software developers work on a project, they interact with existing project artifacts, performing such activities as reading previously filed bug reports in search of duplicate reports. These activities often require a developer to peruse a substantial amount of text. In this paper, we investigate whether it is possible to summarize software artifacts automatically and effectively so that developers could consult smaller summaries instead of entire artifacts. To provide focus to our investigation, we consider the generation of summaries for bug reports. We found that existing conversation-based generators can produce better results than random generators and that a generator trained specifically on bug reports can perform statistically better than existing conversation-based generators. We demonstrate that humans also find these generated summaries reasonable indicating that summaries might be used effectively for many tasks.