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Label prediction on issue tracking systems using text mining

机译：用文本挖掘问题跟踪系统的标签预测

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摘要

Issue tracking systems are overall change-management tools in software development. The issue-solving life cycle is a complex socio-technical activity that requires team discussion and knowledge sharing between members. In that process, issue classification facilitates an understanding of issues and their analysis. Issue tracking systems permit the tagging of issues with default labels (e.g., bug, enhancement) or with customized team labels (e.g., test failures, performance). However, a current problem is that many issues in open-source projects remain unlabeled. The aim of this paper is to improve maintenance tasks in development teams, evaluatingmodels that can suggest a label for an issue using its text comments.We analyze data on issues from several GitHub trending projects, first by extracting issue information and then by applying text mining classifiers (i.e., support vector machine and naive Bayes multinomial). The results suggest that very suitable classifiers may be obtained to label the issues or, at least, to suggest the most suitable candidate labels.
机译：问题跟踪系统是软件开发中的整体变更管理工具。问题解决生命周期是一个复杂的社会技术活动，需要在成员之间进行团队讨论和知识共享。在该过程中，发布分类有助于了解问题及其分析。问题跟踪系统允许使用默认标签（例如，错误，增强）或定制的团队标签标记问题（例如，测试故障，性能）。然而，当前问题是开源项目中的许多问题仍未标记。本文的目的是提高开发团队中的维护任务，可以使用其文本评论来建议一个问题的评估策略。我们通过应用文本挖掘来分析来自多个GitHub趋势项目的问题的数据。分类器（即，支持向量机和天真贝叶斯多行程）。结果表明，可以获得非常合适的分类器来标记问题，或者至少至少建议最合适的候选标签。

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