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Automatic bug labeling using semantic information from LSI

机译：使用LSI的语义信息自动进行错误标记

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摘要

Most open source projects provide a defect tracking system, where users, developers, testers can directly report the problems. The fields provided in the bug report help triager and debugger to understand the problem better. They also help in other tasks like accurate assessment of priority and severity of bugs, identification of appropriate developer to resolve bugs etc. Label field in the bug report is one such field. It has been observed that in many bug repositories, the label field is either not present or is incorrectly assigned. There is a need for automatic bug labeling so that bug reports could be made more informative. This paper presents an automated technique for bug labeling using TF-IDF and LSI. Experimental study shows that there is improvement in results with the addition of semantically similar words obtained from LSI in conjunction with the terms extracted using TF-IDF. Using LSI along with TF-IDF, we achieved 61.5% accuracy for the polish bug reports and 62.8% accuracy for security bug reports as compared to 53.8% accuracy for polish and 61% for security bug reports from using TF-IDF alone.
机译：大多数开源项目都提供一个缺陷跟踪系统，用户，开发人员，测试人员可以在其中直接报告问题。错误报告中提供的字段可帮助Triager和调试器更好地了解问题。它们还帮助完成其他任务，例如准确评估错误的优先级和严重性，确定合适的开发人员以解决错误等。错误报告中的“标签”字段就是这样的字段。已经观察到在许多错误库中，标签字段不存在或分配不正确。需要自动的错误标记，以便可以使错误报告更具信息性。本文提出了一种使用TF-IDF和LSI进行错误标记的自动化技术。实验研究表明，通过添加从LSI获得的语义相似的单词以及使用TF-IDF提取的术语，可以改善结果。通过将LSI与TF-IDF一起使用，我们仅通过使用TF-IDF就能获得61.5％的精确度错误报告和62.8％的准确性，而安全缺陷报告的准确度则为53.8％和61％。

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