自动识别开源项目中非问题类提问，减轻维护者负担。
Labeling questions inside issue trackers
- 通过清洗日志、错误信息等噪声文本，提取有效内容
- 基于分类模型实现自动标注，准确率超81%
- 适合需要管理大量用户提问的开源项目维护团队
开源软件维护者常面临新提交问题的筛选难题。许多用户在问题追踪器中提出本应发在StackOverflow等问答平台的问题，造成垃圾信息泛滥。手动阅读并标记这些无关提问耗时严重。为此，本文首先利用数十种模式清洗问题文本中的日志、堆栈跟踪、环境变量和错误信息等噪声，其次采用基于分类的方法自动标注无关提问。在超过10.2万条记录的数据集上进行实证评估，结果表明该方法可实现超过81%的标注准确率。
原文摘要 · Abstract (English)
One of the issues faced by the maintainers of popular open source software is the triage of newly reported issues. Many of the issues submitted to issue trackers are questions. Many people ask questions on issue trackers about their problem instead of using a proper QA website like StackOverflow. This may seem insignificant but for many of the big projects with thousands of users, this leads to spamming of the issue tracker. Reading and labeling these unrelated issues manually is a serious time consuming task and these unrelated questions add to the burden. In fact, most often maintainers demand to not submit questions in the issue tracker. To address this problem, first, we leveraged dozens of patterns to clean text of issues, we removed noises like logs, stack traces, environment variables, error messages, etc. Second, we have implemented a classification-based approach to automatically label unrelated questions. Empirical evaluations on a dataset of more than 102,000 records show that our approach can label questions with an accuracy of over 81%.
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