When Bugs Linger: A Study of Anomalous Resolution Time Outliers and Their Themes

2025-09-19

AI Summary

This study addresses the problem of abnormally prolonged defect resolution cycles in open-source software, conducting the first cross-project systematic investigation. Methodologically, it proposes a hybrid outlier detection technique integrating Z-score and interquartile range (IQR) to precisely identify long-standing unresolved defect reports; additionally, it applies TF-IDF–based text representation and K-means clustering to model semantic themes in defect summaries. Empirical evaluation across seven major open-source projects reveals that test failures, feature enhancement requests, and user interface issues constitute the three dominant thematic categories of long-delay defects. The work contributes both an interpretable, data-driven foundation for defect prioritization and the first cross-project analysis framework for temporal anomalies in defect resolution—thereby enabling maintainers to focus interventions on high-impact bottlenecks.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Distributed Search
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Identifying unusually long bug resolution times in software
• Analyzing root causes of persistent bugs across projects
• Providing insights to prioritize and address lingering issues

Innovation

• Statistical anomaly detection with Z-score
• TF-IDF for textual feature extraction
• KMeans clustering for bug categorization