When Bugs Linger: A Study of Anomalous Resolution Time Outliers and Their Themes
This work addresses process inefficiencies in software maintenance for open-source project maintainers, though it is incremental as it applies existing statistical and clustering methods to new data.
The study analyzed bug resolution anomalies across seven open-source repositories to identify unusually long resolution times, revealing consistent patterns such as test failures and enhancement requests that can help maintainers prioritize bugs.
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.