BUILD-AND-FIND is a protocol that measures how accurately and efficiently downstream agents can recover hidden repository specifications from AI-generated codebases using accuracy, repeatability, coverage, and effort metrics.
In: IEEE International Confer- ence on Software Maintenance and Evolution
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Reviewer bots' higher comment volume on AI agent PRs is associated with slower resolutions and poorer average feedback quality, while feedback quality itself has no association with PR outcomes.
A learning-based test prioritization method using commit diffs, coverage, and history data outperforms baselines on cross-project validation with Defects4J.
citing papers explorer
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BUILD-AND-FIND: An Effort-Aware Protocol for Evaluating Agent-Managed Codebases
BUILD-AND-FIND is a protocol that measures how accurately and efficiently downstream agents can recover hidden repository specifications from AI-generated codebases using accuracy, repeatability, coverage, and effort metrics.
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On the Footprints of Reviewer Bots Feedback on Agentic Pull Requests in OSS GitHub Repositories
Reviewer bots' higher comment volume on AI agent PRs is associated with slower resolutions and poorer average feedback quality, while feedback quality itself has no association with PR outcomes.
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Commit-Aware Learning-Based Test Case Prioritization for Continuous Integration
A learning-based test prioritization method using commit diffs, coverage, and history data outperforms baselines on cross-project validation with Defects4J.