AgentSZZ is an LLM-agent framework that identifies bug-inducing commits with up to 27.2% higher F1 scores than prior methods by enabling adaptive exploration and causal tracing, especially for cross-file and ghost commits.
Are automated debugging techniques actually helping programmers?
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Empirical evaluation of augmenting statistical fault localization with execution features extracted by EFDD and mapped via random-forest importances on Tests4Py subjects.
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AgentSZZ: Teaching the LLM Agent to Play Detective with Bug-Inducing Commits
AgentSZZ is an LLM-agent framework that identifies bug-inducing commits with up to 27.2% higher F1 scores than prior methods by enabling adaptive exploration and causal tracing, especially for cross-file and ghost commits.
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How do Execution Features Improve Statistical Fault Localization? An Empirical Study
Empirical evaluation of augmenting statistical fault localization with execution features extracted by EFDD and mapped via random-forest importances on Tests4Py subjects.