ImproBR: Bug Report Improver Using LLMs
arXiv cs.AI / 4/30/2026
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Key Points
- The paper introduces ImproBR, an LLM-based pipeline that automatically detects and rewrites low-quality bug reports by filling in missing or unclear sections for Steps to Reproduce (S2R), Observed Behavior (OB), and Expected Behavior (EB).
- ImproBR uses a hybrid approach: a fine-tuned DistilBERT classifier, heuristic checks, and an LLM analyzer orchestrated with GPT-4o mini using section-specific few-shot prompts.
- It further improves accuracy by applying Retrieval-Augmented Generation (RAG) backed by Minecraft Wiki domain knowledge.
- In evaluations on Mojira (139 challenging real-world reports), ImproBR dramatically boosts structural completeness (7.9% to 96.4%), increases executable S2R (28.8% to 67.6%), and raises fully reproducible bug reports (1 to 13).
- The results suggest that LLM-assisted bug triage can substantially reduce back-and-forth between users and developers and make reports actionable sooner.
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