# ImproBR: Bug Report Improver Using LLMs

arXiv: https://arxiv.org/abs/2604.26142 · HTML: https://mlsanity.com/paper/2604.26142/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Text and Code Embeddings by Contrastive Pre-Training](https://mlsanity.com/paper/2201.10005/) (2022)
- [Assessing the Quality of the Steps to Reproduce in Bug Reports](https://mlsanity.com/paper/1906.07107/) (2019)
- [Auto-completing Bug Reports for Android Applications](https://mlsanity.com/paper/1705.04016/) (2017)
- [BURT: A Chatbot for Interactive Bug Reporting](https://mlsanity.com/paper/2302.06050/) (2023)
- [Agents in the Sandbox: End-to-End Crash Bug Reproduction for Minecraft](https://mlsanity.com/paper/2503.20036/) (2025)
- [Can We Enhance Bug Report Quality Using LLMs?: An Empirical Study of LLM-Based Bug Report Generation](https://mlsanity.com/paper/2504.18804/) (2025)

## Built on by (most cited first, 2 of 2)

- [Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reports](https://mlsanity.com/paper/2605.17561/) (2026)
- [Writing Bug Reports for Software Repair Agents: What Information Matters Most?](https://mlsanity.com/paper/2607.09553/) (2026)

## Abstract (arXiv)

Bug tracking systems play a crucial role in software maintenance, yet developers frequently struggle with low-quality user-submitted reports that omit essential details such as Steps to Reproduce (S2R), Observed Behavior (OB), and Expected Behavior (EB). We propose ImproBR, an LLM-based pipeline that automatically detects and improves bug reports by addressing missing, incomplete, and ambiguous S2R, OB, and EB sections. ImproBR employs a hybrid detector combining fine-tuned DistilBERT, heuristic analysis, and an LLM analyzer, guided by GPT-4o mini with section-specific few-shot prompts and a Retrieval-Augmented Generation (RAG) pipeline grounded in Minecraft Wiki domain knowledge. Evaluated on Mojira, ImproBR improved structural completeness from 7.9% to 96.4%, more than doubled the proportion of executable S2R from 28.8% to 67.6%, and raised fully reproducible bug reports from 1 to 13 across 139 challenging real-world reports.