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

arXiv: https://arxiv.org/abs/2509.16140 · HTML: https://mlsanity.com/paper/2509.16140/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [Bug or Not? Bug Report Classification Using N-Gram IDF](https://mlsanity.com/paper/1709.05763/) (2017)
- [Auto-labelling of Bug Report using Natural Language Processing](https://mlsanity.com/paper/2212.06334/) (2022)
- [DABT: A Dependency-aware Bug Triaging Method](https://mlsanity.com/paper/2104.12744/) (2021)
- [S-DABT: Schedule and Dependency-Aware Bug Triage in Open-Source Bug Tracking Systems](https://mlsanity.com/paper/2204.05972/) (2022)
- [GitBugs: Bug Reports for Duplicate Detection, Retrieval Augmented Generation, Triage, and More](https://mlsanity.com/paper/2504.09651/) (2025)
- [Automatic Classification of Bug Reports Based on Multiple Text Information and Reports' Intention](https://mlsanity.com/paper/2208.01274/) (2022)
- [Automated Duplicate Bug Report Detection in Large Open Bug Repositories](https://mlsanity.com/paper/2504.14797/) (2025)

## Abstract (arXiv)

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.