软件项目中预测问题成功的问题描述和注释
Descriptions of issues and comments for predicting issue success in software projects
摘要：
软件开发任务必须成功地执行，以达到软件质量和客户满意度。了解软件任务是否可能失败对于确保软件项目的成功至关重要。问题跟踪系统存储软件任务（问题）和评论的信息，这可能有助于预测问题的成功;然而，几乎没有关于这个主题的研究存在。这项工作研究的有用性的文字描述的问题和评论，预测问题是否会得到成功解决或不。从四个流行的问题跟踪系统中提取了588个软件项目的问题和评论。7个机器学习分类器在30 k个问题和超过120 k条评论上进行了训练，并进行了超过6000次实验，以预测三种类型问题的成功：错误，改进和新功能。结果提供的证据表明，问题和评论的描述是有用的预测问题的成功与超过85%的准确性和精确度，并随着时间的推移，问题的成功预测不同。与软件开发相关的词语与预测问题成功特别相关。其他沟通方面及其与软件项目成功的关系必须使用来自软件工具的数据进行详细研究。
英文摘要：
Software development tasks must be performed successfully to achieve software quality and customer satisfaction. Knowing whether software tasks are likely to fail is essential to ensure the success of software projects. Issue Tracking Systems store information of software tasks (issues) and comments, which can be useful to predict issue success; however; almost no research on this topic exists. This work studies the usefulness of textual descriptions of issues and comments for predicting whether issues will be resolved successfully or not. Issues and comments of 588 software projects were extracted from four popular Issue Tracking Systems. Seven machine learning classifiers were trained on 30k issues and more than 120k comments, and more than 6000 experiments were performed to predict the success of three types of issues: bugs, improvements and new features. The results provided evidence that descriptions of issues and comments are useful for predicting issue success with more than 85% of accuracy and precision, and that the predictions of issue success vary over time. Words related to software development were particularly relevant for predicting issue success. Other communication aspects and their relationship to the success of software projects must be researched in detail using data from software tools.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
