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Modelling the 'hurried' bug report reading process to summarize bug reports
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AI
DOI：10.1007/s10664-014-9311-2.png)
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Abstract
En 中文
Although bug reports are frequently consulted project assets, they are communication logs, by-products of bug resolution, and not artifacts created with the intent of being easy to follow. To facilitate bug report digestion, we propose a new, unsupervised, bug report summarization approach that estimates the attention a user would hypothetically give to different sentences in a bug report, when pressed with time. We pose three hypotheses on what makes a sentence relevant: discussing frequently discussed topics, being evaluated or assessed by other sentences, and keeping focused on the bug report's title and description. Our results suggest that our hypotheses are valid, since the summaries have as much as 12 % improvement in standard summarization evaluation metrics compared to the previous approach. Our evaluation also asks developers to assess the quality and usefulness of the summaries created for bug reports they have worked on. Feedback from developers not only shows the summaries are useful, but also points out important requirements for this, and any bug summarization approach, and indicates directions for future work.
Keywords:
Bug reports
Summarization
Natural language processing
Empirical study
Productivity
Journal
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