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LLMVul: A Vulnerability-Labeled Dataset of LLM-Generated C/C++ Functions from Real Production Repositories
Mohammad Farhad, Shuvalaxmi Dass
Large language models (LLMs) are increasingly used to generate and assist with software development, yet existing vulnerability datasets largely focus on human-written code or cont…
Residual Risk Assessment in Benign Code: How Far Are We? A Multi-Model Semantic and Structural Similarity Approach
Mohammad Farhad, Shuvalaxmi Dass
Software security assurance relies on effective vulnerability detection and patching, yet determining whether a patch fully eliminates risk remains an underexplored challenge. Exis…
Evolutionary Defense: Advancing Moving Target Strategies with Bio-Inspired Reinforcement Learning to Secure Misconfigured Software Applications
Niloofar Heidarikohol, Shuvalaxmi Dass, Akbar Siami Namin
Improper configurations in software systems often create vulnerabilities, leaving them open to exploitation. Static architectures exacerbate this issue by allowing misconfiguration…
Vulnerability Coverage as an Adequacy Testing Criterion
Shuvalaxmi Dass, Akbar Siami Namin
Mainstream software applications and tools are the configurable platforms with an enormous number of parameters along with their values. Certain settings and possible interactions…
Detection of Coincidentally Correct Test Cases through Random Forests
Shuvalaxmi Dass, Xiaozhen Xue, Akbar Siami Namin
The performance of coverage-based fault localization greatly depends on the quality of test cases being executed. These test cases execute some lines of the given program and deter…