mlsanityData to 7 Oct 2026

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

arXiv 2510.08005 ↗First posted 9 Oct 2025cs.SE cs.AIcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 54 of its 182 references that are in the map. First posted on arXiv 9 Oct 2025.

Builds on

54 of 182 references.

References · 54

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
  5. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  6. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  7. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  8. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  9. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  10. 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 956
  11. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 389
  12. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 329
38 more
  1. 2024AI Agents That Mattercited by 199
  2. 2024The Emerged Security and Privacy of LLM Agent: A Survey with Case Studiescited by 127
  3. 2022Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproductioncited by 110
  4. 2025Unveiling Privacy Risks in LLM Agent Memorycited by 99
  5. 2024LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigationcited by 92
  6. 2025HalluLens: LLM Hallucination Benchmarkcited by 88
  7. 2018BLEU is Not Suitable for the Evaluation of Text Simplificationcited by 78
  8. 2023Large Language Models for Test-Free Fault Localizationcited by 67
  9. 2024Agents in Software Engineering: Survey, Landscape, and Visioncited by 60
  10. 2025The Obvious Invisible Threat: LLM-Powered GUI Agents' Vulnerability to Fine-Print Injectionscited by 55
  11. 2023A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localizationcited by 50
  12. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 45
  13. 2023Explainable Automated Debugging via Large Language Model-driven Scientific Debuggingcited by 40
  14. 2024TESTEVAL: Benchmarking Large Language Models for Test Case Generationcited by 38
  15. 2018The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Modelscited by 29
  16. 2025LLM Agents Should Employ Security Principlescited by 28
  17. 2024Large Language Models as Test Case Generators: Performance Evaluation and Enhancementcited by 27
  18. 2025Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applicationscited by 23
  19. 2025Agentic Bug Reproduction for Effective Automated Program Repair at Googlecited by 20
  20. 2025Unified Software Engineering Agent as AI Software Engineercited by 17
  21. 2016Precise Condition Synthesis for Program Repaircited by 13
  22. 2019Assessing the Quality of the Steps to Reproduce in Bug Reportscited by 13
  23. 2023Evaluating Diverse Large Language Models for Automatic and General Bug Reproductioncited by 12
  24. 2023Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoningcited by 11
  25. 2024A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?cited by 9
  26. 2025LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurationscited by 9
  27. 2024Feedback-Driven Automated Whole Bug Report Reproduction for Android Appscited by 7
  28. 2017Automatic Bug Triage using Semi-Supervised Text Classificationcited by 6
  29. 2024Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Studycited by 6
  30. 2024Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMwarecited by 6
  31. 2023BURT: A Chatbot for Interactive Bug Reportingcited by 5
  32. 2024Natural Language Processing for Requirements Traceabilitycited by 5
  33. 2018Traceability in the Wild: Automatically Augmenting Incomplete Trace Linkscited by 4
  34. 2022BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chatscited by 4
  35. 2022Toward Interactive Bug Reporting for (Android App) End-Userscited by 4
  36. 2025An Empirical Study on Leveraging Images in Automated Bug Report Reproductioncited by 4
  37. 2025Agents in the Sandbox: End-to-End Crash Bug Reproduction for Minecraftcited by 4
  38. 2025Can We Enhance Bug Report Quality Using LLMs?: An Empirical Study of LLM-Based Bug Report Generationcited by 3

50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025SweRank+: Multilingual, Multi-Turn Code Ranking for Software Issue Localizationcited by 4
  2. 2026Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reportscited by 1
  3. 2026What Makes a Good Bug Report for an AI Agent?cited by 1
  4. 2026Protocol-Preserving Context Trimming for Agentic Workflows: Benefits, Failure Regimes, and Budget Guardrailscited by 0