# Past, Present, and Future of Bug Tracking in the Generative AI Era

arXiv: https://arxiv.org/abs/2510.08005 · HTML: https://mlsanity.com/paper/2510.08005/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 54 of its 182 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 30 of 54)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [AI Agents That Matter](https://mlsanity.com/paper/2407.01502/) (2024)
- [The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies](https://mlsanity.com/paper/2407.19354/) (2024)
- [Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction](https://mlsanity.com/paper/2209.11515/) (2022)
- [Unveiling Privacy Risks in LLM Agent Memory](https://mlsanity.com/paper/2502.13172/) (2025)
- [LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation](https://mlsanity.com/paper/2409.20550/) (2024)
- [HalluLens: LLM Hallucination Benchmark](https://mlsanity.com/paper/2504.17550/) (2025)
- [BLEU is Not Suitable for the Evaluation of Text Simplification](https://mlsanity.com/paper/1810.05995/) (2018)
- [Large Language Models for Test-Free Fault Localization](https://mlsanity.com/paper/2310.01726/) (2023)
- [Agents in Software Engineering: Survey, Landscape, and Vision](https://mlsanity.com/paper/2409.09030/) (2024)
- [The Obvious Invisible Threat: LLM-Powered GUI Agents' Vulnerability to Fine-Print Injections](https://mlsanity.com/paper/2504.11281/) (2025)
- [A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization](https://mlsanity.com/paper/2308.05487/) (2023)
- [Large Language Models Hallucination: A Comprehensive Survey](https://mlsanity.com/paper/2510.06265/) (2025)
- [Explainable Automated Debugging via Large Language Model-driven Scientific Debugging](https://mlsanity.com/paper/2304.02195/) (2023)
- [TESTEVAL: Benchmarking Large Language Models for Test Case Generation](https://mlsanity.com/paper/2406.04531/) (2024)
- [The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models](https://mlsanity.com/paper/1801.10269/) (2018)
- [LLM Agents Should Employ Security Principles](https://mlsanity.com/paper/2505.24019/) (2025)
- [Large Language Models as Test Case Generators: Performance Evaluation and Enhancement](https://mlsanity.com/paper/2404.13340/) (2024)
- [Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applications](https://mlsanity.com/paper/2504.21030/) (2025)

## Built on by (most cited first, 4 of 4)

- [SweRank+: Multilingual, Multi-Turn Code Ranking for Software Issue Localization](https://mlsanity.com/paper/2512.20482/) (2025)
- [Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reports](https://mlsanity.com/paper/2605.17561/) (2026)
- [What Makes a Good Bug Report for an AI Agent?](https://mlsanity.com/paper/2607.07593/) (2026)
- [Protocol-Preserving Context Trimming for Agentic Workflows: Benefits, Failure Regimes, and Budget Guardrails](https://mlsanity.com/paper/2609.16461/) (2026)

## Abstract (arXiv)

Traditional bug-tracking systems rely heavily on manual reporting, reproduction, classification, and resolution, involving multiple stakeholders such as end users, customer support, developers, and testers. This division of responsibilities requires substantial coordination and human effort, widens the communication gap between non-technical users and developers, and significantly slows the process from bug discovery to deployment. Moreover, current solutions are highly asynchronous, often leaving users waiting long periods before receiving any feedback. In this paper, we examine the evolution of bug-tracking practices, from early paper-based methods to today's web-based platforms, and present a forward-looking vision of an AI-powered bug tracking framework. The framework augments existing systems with large language model (LLM) and agent-driven automation, and we report early adaptations of its key components, providing initial empirical grounding for its feasibility. The proposed framework aims to reduce time to resolution and coordination overhead by enabling end users to report bugs in natural language while AI agents refine reports, attempt reproduction, classify bugs, validate reports, suggest no-code fixes, generate patches, and support continuous integration and deployment. We discuss the challenges and opportunities of integrating LLMs into bug tracking and show how intelligent automation can transform software maintenance into a more efficient, collaborative, and user-centric process.