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As large language model agents increasingly compile natural-language requirements into repository-level code, requirement-to-test generation grows in importance to provide validation constraints (or harness) to guide code generation. Yet it remains unclear whether current agents can generate tests that validate the req...
VIC-RAGENT is proposed, an LLM-based multi-agent framework for effective and explainable vulnerability detection that employs a multi-stage reasoning process that progressively refines candidate vulnerabilities through preliminary inspection, reanalysis, and a final decision stage.
Test4Py is presented, a novel framework that enhances type correctness in automated test generation for Python by leveraging the program’s call graph to capture richer contextual information about parameters, and introducing a behavior-based type inference mechanism that accurately infers parameter types and constructs...
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