# Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reports

arXiv: https://arxiv.org/abs/2605.17561 · HTML: https://mlsanity.com/paper/2605.17561/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 72 references that are in the map.
- First posted on arXiv 17 May 2026.

## Builds on (most cited first, 23 of 23)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [GLM-5: from Vibe Coding to Agentic Engineering](https://mlsanity.com/paper/2602.15763/) (2026)
- [Kimi K2.5: Visual Agentic Intelligence](https://mlsanity.com/paper/2602.02276/) (2026)
- [WebThinker: Empowering Large Reasoning Models with Deep Research Capability](https://mlsanity.com/paper/2504.21776/) (2025)
- [How Language Model Hallucinations Can Snowball](https://mlsanity.com/paper/2305.13534/) (2023)
- [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024)
- [Why Language Models Hallucinate](https://mlsanity.com/paper/2509.04664/) (2025)
- [Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools](https://mlsanity.com/paper/2502.04644/) (2025)
- [WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents](https://mlsanity.com/paper/2509.06501/) (2025)
- [SpecRover: Code Intent Extraction via LLMs](https://mlsanity.com/paper/2408.02232/) (2024)
- [From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents](https://mlsanity.com/paper/2506.18959/) (2025)
- [ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues?](https://mlsanity.com/paper/2507.05639/) (2025)
- [Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches](https://mlsanity.com/paper/2510.04905/) (2025)
- [BrowseMaster: Towards Scalable Web Browsing via Tool-Augmented Programmatic Agent Pair](https://mlsanity.com/paper/2508.09129/) (2025)
- [A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text Classification](https://mlsanity.com/paper/2504.18884/) (2025)
- ["Won't We Fix this Issue?" Qualitative Characterization and Automated Identification of Wontfix Issues on GitHub](https://mlsanity.com/paper/1904.02414/) (2019)
- [Past, Present, and Future of Bug Tracking in the Generative AI Era](https://mlsanity.com/paper/2510.08005/) (2025)
- [GitBugs: Bug Reports for Duplicate Detection, Retrieval Augmented Generation, Triage, and More](https://mlsanity.com/paper/2504.09651/) (2025)
- [RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data](https://mlsanity.com/paper/2510.08667/) (2025)
- [ImproBR: Bug Report Improver Using LLMs](https://mlsanity.com/paper/2604.26142/) (2026)
- [Automated Bug Triaging using Instruction-Tuned Large Language Models](https://mlsanity.com/paper/2508.21156/) (2025)

## Built on by (most cited first, 1 of 1)

- [Can LLMs Fix It Without Code? Toward Automated Verification of No-Code Bug Fixes](https://mlsanity.com/paper/2610.11963/) (2026)

## Abstract (arXiv)

Issues faced when using software are reported in the form of bug reports. However, many bug reports are invalid, meaning they do not require code changes, and are resolved with a no-code fix. Manually determining the root cause of the invalid bug reports and providing actionable resolutions by the customer support causes a serious waste of resources. Our goal is to introduce a standardized taxonomy for root-cause oriented invalid bug report subclassification, and perform experiments to test the accuracy of various approaches on invalid subclassification and no-code fix generation. We study how different configurations perform on a gold-standard benchmark we have created. Using a manually curated benchmark for higher quality analysis, we experimented with vanilla LLMs, Retrieval Augmented Generation, and agentic web search to identify invalid subclasses and generate no-code fixes. We evaluated the results against manually labeled ground truth data that includes the invalid subclass and no-code fixes from the original bug reports. We measured subclass detection performance with weighted F1-Score, and assessed no-code fix suggestions using BERTScore and Judge LLM success rates. For subclassification, retrieval augmented generation achieves the highest overall performance with 0.66 weighted F1, slightly outperforming vanilla LLMs at 0.65 and agentic web search at 0.64. At the subclass level, performance peaks at 0.85 F1 for Non-reproducibility and 0.79 for Feature Request and Question, while Wrong Version remains the most challenging with scores between 0.00 and 0.29. For no-code fix generation, agentic web search achieves the highest overall Judge LLM success rate at 68.9%, compared to 64.4% for RAG applications and 64.9% for vanilla LLMs, with subclass-level peaks of 87.4% for Working as Designed and 72.2% for Question.