# Learning Software Bug Reports: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2507.04422 · HTML: https://mlsanity.com/paper/2507.04422/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 204 references that are in the map.
- First posted on arXiv 6 Jul 2025.

## Builds on (most cited first, 30 of 33)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Do Performance Aspirations Matter for Guiding Software Configuration Tuning?](https://mlsanity.com/paper/2301.03290/) (2023)
- [DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging](https://mlsanity.com/paper/1801.01275/) (2018)
- [MMO: Meta Multi-Objectivization for Software Configuration Tuning](https://mlsanity.com/paper/2112.07303/) (2021)
- [FEMOSAA: Feature Guided and Knee Driven Multi-Objective Optimization for Self-Adaptive Software](https://mlsanity.com/paper/1608.08933/) (2016)
- [Self-Adaptive Trade-off Decision Making for Autoscaling Cloud-Based Services](https://mlsanity.com/paper/1608.05917/) (2016)
- [How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance](https://mlsanity.com/paper/2002.09040/) (2020)
- [The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering](https://mlsanity.com/paper/2202.03728/) (2022)
- [Predicting Configuration Performance in Multiple Environments with Sequential Meta-learning](https://mlsanity.com/paper/2402.03183/) (2024)
- [Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?](https://mlsanity.com/paper/2201.07096/) (2022)
- [Predicting Software Performance with Divide-and-Learn](https://mlsanity.com/paper/2306.06651/) (2023)
- [Adapting Multi-objectivized Software Configuration Tuning](https://mlsanity.com/paper/2404.04744/) (2024)
- [Distilled Lifelong Self-Adaptation for Configurable Systems](https://mlsanity.com/paper/2501.00840/) (2025)
- [Predicting Issue Types on GitHub](https://mlsanity.com/paper/2107.09936/) (2021)
- [Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning](https://mlsanity.com/paper/2501.01876/) (2025)
- [Faster Configuration Performance Bug Testing with Neural Dual-level Prioritization](https://mlsanity.com/paper/2501.15392/) (2025)
- [Automatically Matching Bug Reports With Related App Reviews](https://mlsanity.com/paper/2102.07134/) (2021)
- [Duplicate Bug Report Detection: How Far Are We?](https://mlsanity.com/paper/2212.00548/) (2022)
- [Deep Configuration Performance Learning: A Systematic Survey and Taxonomy](https://mlsanity.com/paper/2403.03322/) (2024)
- [On Reporting Performance and Accuracy Bugs for Deep Learning Frameworks: An Exploratory Study from GitHub](https://mlsanity.com/paper/2204.07893/) (2022)
- [The Same Only Different: On Information Modality for Configuration Performance Analysis](https://mlsanity.com/paper/2501.15475/) (2025)
- ["Won't We Fix this Issue?" Qualitative Characterization and Automated Identification of Wontfix Issues on GitHub](https://mlsanity.com/paper/1904.02414/) (2019)
- [Towards Effective Bug Triage with Towards Effective Bug Triage with Software Data Reduction Techniques](https://mlsanity.com/paper/1704.04761/) (2017)
- [On Usefulness of the Deep-Learning-Based Bug Localization Models to Practitioners](https://mlsanity.com/paper/1907.08588/) (2019)
- [A Spatial-Temporal Graph Neural Network Framework for Automated Software Bug Triaging](https://mlsanity.com/paper/2101.11846/) (2021)
- [DABT: A Dependency-aware Bug Triaging Method](https://mlsanity.com/paper/2104.12744/) (2021)
- [BoostNSift: A Query Boosting and Code Sifting Technique for Method Level Bug Localization](https://mlsanity.com/paper/2108.12901/) (2021)
- [S-DABT: Schedule and Dependency-Aware Bug Triage in Open-Source Bug Tracking Systems](https://mlsanity.com/paper/2204.05972/) (2022)

## Built on by (most cited first, 3 of 3)

- [Past, Present, and Future of Bug Tracking in the Generative AI Era](https://mlsanity.com/paper/2510.08005/) (2025)
- [Progressive Code Integration for Abstractive Bug Report Summarization](https://mlsanity.com/paper/2512.00325/) (2025)
- [Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning](https://mlsanity.com/paper/2604.18862/) (2026)

## Abstract (arXiv)

The recent advancement of artificial intelligence, especially machine learning (ML), has significantly impacted software engineering research, including bug report analysis. ML aims to automate the understanding, extraction, and correlation of information from bug reports. Despite its growing importance, there has been no comprehensive review in this area. In this paper, we present a systematic literature review covering 1,825 papers, selecting 204 for detailed analysis. We derive seven key findings: 1) Extensive use of CNN, LSTM, and kNN for bug report analysis, with advanced models like BERT underutilized due to their complexity. 2) Word2Vec and TF-IDF are popular for feature representation, with a rise in deep learning approaches. 3) Stop word removal is the most common preprocessing, with structural methods rising after 2020. 4) Eclipse and Mozilla are the most frequently evaluated software projects. 5) Bug categorization is the most common task, followed by bug localization and severity prediction. 6) There is increasing attention on specific bugs like non-functional and performance bugs. 7) Common evaluation metrics are F1-score, Recall, Precision, and Accuracy, with k-fold cross-validation preferred for model evaluation. 8) Many studies lack robust statistical tests. We also identify six promising future research directions to provide useful insights for practitioners.