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Predicting severity of bug report by mining bug repository with concept profile

机译:通过使用概念概要文件挖掘错误存储库来预测错误报告的严重性

摘要

Recently, for large scale software projects, developers rely on bug reports for corrective software maintenance. The severity of a reported bug is an important feature to decide how fast it needs to be fixed. Therefore, to arrange a new submitted bug to an appropriate fixer, it is necessary to recognize the severity of each bug report. Unfortunately, reporters need to decide the severity of bugs manually. Even if there are guidelines on how to verify the severity of a bug, it is still a time-consuming work. Utilizing the concept profiles by mining bug repositories is a good way to resolve this problem. In this paper, we propose a concept profile-based prediction technique to assign the severity of a given bug. In detail, we analyze historical bug reports in the bug repositories and build the concept profiles from them. We evaluate the performance of our method on the bug reports from the bug repositories of popular open-source projects that include Eclipse and Mozilla Firefox, the result shows that the proposed technique can effectively predict the severity of a given bug.
机译:最近,对于大型软件项目,开发人员依靠错误报告来进行软件纠正维护。报告的错误的严重性是决定修复它的速度的重要功能。因此,要将新提交的错误安排给适当的修复程序,有必要识别每个错误报告的严重性。不幸的是,记者需要手动确定漏洞的严重性。即使有关于如何验证错误严重性的准则,这仍然是一项耗时的工作。通过挖掘错误存储库来使用概念概要文件是解决此问题的好方法。在本文中,我们提出了一种基于概念概貌的预测技术来分配给定漏洞的严重性。详细地,我们分析错误存储库中的历史错误报告,并从中构建概念档案。我们从包括Eclipse和Mozilla Firefox在内的流行开源项目的错误存储库中的错误报告中评估了该方法的性能,结果表明,所提出的技术可以有效地预测给定错误的严重性。

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