ConcoLixir uses a reactive LLM oracle to improve line coverage in Python concolic testing by 8.6 to 17 percentage points on synthetic, real-world, and library targets.
Improving change prediction with fine-grained source code mining,
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Empirical comparison on 10 Node.js apps finds only 22% overlap between history-based and dynamic change-impact candidates, with dynamic analysis more precise and history adding missed candidates, supporting hybrid use.
citing papers explorer
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ConcoLixir: Reactive LLM Discovery Oracles for Python Concolic Testing
ConcoLixir uses a reactive LLM oracle to improve line coverage in Python concolic testing by 8.6 to 17 percentage points on synthetic, real-world, and library targets.
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Change Impact Recommendation for JavaScript: Lessons from History and Runtime Analysis
Empirical comparison on 10 Node.js apps finds only 22% overlap between history-based and dynamic change-impact candidates, with dynamic analysis more precise and history adding missed candidates, supporting hybrid use.