When Bugs Linger: A Study of Anomalous Resolution Time Outliers and Their Themes
Avinash Patil
TL;DR
Prolonged bug-resolution times signal process inefficiencies in large-scale open-source software. The authors propose a unified pipeline that blends $Z$-score and $IQR$-based anomaly detection with TF-IDF representations and $K$-means clustering (aided by $PCA$) to identify and interpret outlier bug reports, using the GitBugs dataset across seven repositories. The results show that anomalies cluster around test instability, infrastructure/dependency issues, and UI problems, with an observable push toward earlier lifecycle stages. These insights enable anomaly-aware triage and maintenance prioritization and motivate integrating such analytics into issue-tracking systems for proactive software maintenance.
Abstract
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.