Developers use bug reports to triage and fix bugs. When triaging a bug report, developers must decide whether the bug report is valid (i.e., a real bug). A large amount of bug reports are submitted every day, with many of them end up being invalid reports. Manually determining valid bug report is a difficult and tedious task. Thus, an approach that can automatically analyze the validity of a bug report and determine whether a report is valid can help developers prioritize their triaging tasks and avoid wasting time and effort on invalid bug reports. In this study, motivated by the above needs, we propose an approach which can determine whether a newly submitted bug report is valid. Our approach first extracts 33 features from bug reports. The extracted features are grouped along 5 dimensions, i.e., reporter experience, collaboration network, completeness, readability and text. Based on these features, we use a random forest classifier to identify valid bug reports. To evaluate the effectiveness of our approach, we experiment on large-scale datasets containing a total of 560,697 bug reports from five open source projects (i.e., Eclipse, Netbeans, Mozilla, Firefox and Thunderbird). On average, across the five datasets, our approach achieves an F1-score for valid bug reports and F1-score for invalid ones of 0.74 and 0.67, respectively. Moreover, our approach achieves an average AUC of 0.81. In terms of AUC and F1-scores for valid and invalid bug reports, our approach statistically significantly outperforms two baselines using features that are proposed by Zanetti et al. [104] . We also study the most important features that distinguish valid bug reports from invalid ones. We find that the textual features of a bug report and reporter's experience are the most important factors to distinguish valid bug reports from invalid ones.

中文翻译：

---

来自小麦的糠秕：表征和确定有效的错误报告

开发人员使用错误报告来分类和修复错误。在对错误报告进行分类时，开发人员必须决定错误报告是否是有效的（即，一个真正的错误）。每天提交大量错误报告，其中许多最终被无效的报告。手动确定有效的错误报告是一项艰巨而乏味的任务。因此，一种可以自动分析错误报告的有效性并确定报告是否正确的方法有效的 可以帮助开发人员优先处理他们的分类任务，避免浪费时间和精力 无效的错误报告。在本研究中，出于上述需求，我们提出了一种方法来确定新提交的错误报告是否是有效的. 我们的方法首先从错误报告中提取 33 个特征。提取的特征按5个维度分组，即记者体验、协作网络、完整性、可读性和文本。基于这些特征，我们使用随机森林分类器来识别有效的错误报告。为了评估我们方法的有效性，我们对包含来自五个开源项目（即 Eclipse、Netbeans、Mozilla、Firefox 和 Thunderbird）的总共 560,697 个错误报告的大规模数据集进行了实验。平均而言，在五个数据集中，我们的方法实现了 F1 分数有效的 错误报告和 F1 分数 无效的分别为 0.74 和 0.67。此外，我们的方法实现了 0.81 的平均 AUC。就 AUC 和 F1 分数而言有效的 和 无效的 错误报告，我们的方法在统计上显着优于使用 Zanetti 等人提出的特征的两个基线。 [104]. 我们还研究了区分的最重要的特征有效的 错误报告来自 无效的那些。我们发现错误报告的文本特征和报告者的经验是区分的最重要因素有效的 错误报告来自 无效的 那些。