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首页> 外文会议>IEEE International Conference on Software Maintenance and Evolution >A Simple NLP-Based Approach to Support Onboarding and Retention in Open Source Communities
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A Simple NLP-Based Approach to Support Onboarding and Retention in Open Source Communities

机译：一种简单的基于NLP的方法来支持开源社区的船上和保留

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摘要

Successful open source communities are constantly looking for new members and helping them become active developers. A common approach for developer onboarding in open source projects is to let newcomers focus on relevant yet easy-to-solve issues to familiarize themselves with the code and the community. The goal of this research is twofold. First, we aim at automatically identifying issues that newcomers can resolve by analyzing the history of resolved issues by simply using the title and description of issues. Second, we aim at automatically identifying issues, that can be resolved by newcomers who later become active developers. We mined the issue trackers of three large open source projects and extracted natural language features from the title and description of resolved issues. In a series of experiments, we optimized and compared the accuracy of four supervised classifiers to address our research goals. Random Forest, achieved up to 91% precision (F1-score 72%) towards the first goal while for the second goal, Decision Tree achieved a precision of 92% (F1-score 91%). A qualitative evaluation gave insights on what information in the issue description is helpful for newcomers. Our approach can be used to automatically identify, label, and recommend issues for newcomers in open source software projects based only on the text of the issues.
机译：成功的开源社区不断寻找新成员，并帮助他们成为活动开发人员。开源项目中的开发人员的常见方法是让新人侧重于相关但易于解决的问题，以熟悉代码和社区。这项研究的目标是双重。首先，我们的目标是通过简单地使用问题的标题和描述来自动识别新移民可以通过分析解决问题的历史来解决问题。其次，我们的目标是自动识别问题，可以通过后来成为活动开发人员的新人解决。我们挖掘了三个大型开源项目的问题跟踪器，并从解决问题的标题和描述中提取了自然语言功能。在一系列实验中，我们优化并比较了四个监督分类器的准确性来解决我们的研究目标。随机森林，达到91％的精确度（F1-Score 72％）在第二次目标的同时，决策树实现了92％的精确度（F1分数91％）。定性评估对问题描述中的信息有助于新人来说。我们的方法可用于自动识别，标签并为开源软件项目中的新人推荐问题，并仅基于问题的文本。

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