Efficient bug resolution is critical for maintaining software quality and user satisfaction. However, specific bug reports experience unusually long resolution times, which may indicate underlying process inefficiencies or complex issues. This study presents a comprehensive analysis of bug resolution anomalies across seven prominent open-source repositories: Cassandra, Firefox, Hadoop, HBase, SeaMonkey, Spark, and Thunderbird. Utilizing statistical methods such as Z-score and Interquartile Range (IQR), we identify anomalies in bug resolution durations. To understand the thematic nature of these anomalies, we apply Term Frequency-Inverse Document Frequency (TF-IDF) for textual feature extraction and KMeans clustering to group similar bug summaries. Our findings reveal consistent patterns across projects, with anomalies often clustering around test failures, enhancement requests, and user interface issues. This approach provides actionable insights for project maintainers to prioritize and effectively address long-standing bugs.

翻译：高效的缺陷解决对于维护软件质量和用户满意度至关重要。然而，特定缺陷报告会经历异常漫长的解决时间，这可能暗示着潜在流程低效或复杂问题。本研究对七个知名开源代码库（Cassandra、Firefox、Hadoop、HBase、SeaMonkey、Spark 和 Thunderbird）中的缺陷解决异常进行了全面分析。利用 Z 分数和四分位距等统计方法，我们识别了缺陷解决时长中的异常值。为理解这些异常的主题性质，我们应用词频-逆文档频率进行文本特征提取，并使用 KMeans 聚类对相似的缺陷摘要进行分组。研究结果揭示了跨项目的一致模式：异常通常围绕测试失败、功能增强请求和用户界面问题聚集。该方法为项目维护者提供了可操作的见解，以优先处理并有效解决长期存在的缺陷。