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.
Automated Identification of Security Issues from Commit Messages and Bug Reports,
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The authors present a registered report outlining their planned large-scale empirical study of vulnerability communication in pull requests by different account types.
citing papers explorer
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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.
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How Humans, Bots, and Agents Communicate About Vulnerabilities in Pull Requests
The authors present a registered report outlining their planned large-scale empirical study of vulnerability communication in pull requests by different account types.