ImproBR combines a hybrid detector with GPT-4o mini and RAG to raise bug report structural completeness from 7.9% to 96.4% and executable steps from 28.8% to 67.6% on 139 Mojira reports.
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MNAL reduces human effort in bug report labeling by up to 95.8% for readability and 196% for identifiability while improving identification performance and working with various neural models.
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ImproBR: Bug Report Improver Using LLMs
ImproBR combines a hybrid detector with GPT-4o mini and RAG to raise bug report structural completeness from 7.9% to 96.4% and executable steps from 28.8% to 67.6% on 139 Mojira reports.
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Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning
MNAL reduces human effort in bug report labeling by up to 95.8% for readability and 196% for identifiability while improving identification performance and working with various neural models.