This research investigates Large Language Models (LLMs), which are considered among the most performant AI models to date, for the CVD task, and high-light the importance of tuning to resolve the task, the performance of Double tuning, as well as the potential of Llama models for CVD.
Large language models (LLMs) are increasingly applied to the automated repair of C/C++ security vulnerabilities, and compile rate is a commonly reported proxy for progress: whether the generated patch compiles. We argue that compile rate is a scientifically unreliable metric for single-function vulnerability repair, an...
Om Nepal, Sushant Aryal, Oluseyi Olukola et al.· 0 citations
Goanna is introduced, a novel type checker for Haskell that focuses on improving error diagnostics, and shows performance constraints when diagnosing large programs containing complex errors, but remains responsive enough to provide real-time debugging assistance for small to medium-sized programs.
Shuai Fu, Tim Dwyer, Peter James Stuckey et al.· International Conference on...· 0 citations
This paper presents a formal mathematical model for categorizing the outcome of generated-tests into four classes, a couple of basic metrics: Bug-Revealing Rate (BRR) and Bug-Validating Rate (BVR); and two basic statistical tests to ensure that the results are rigorous.
Zeyad Farooq Lutfi· Al-Noor Journal of Engineeri...· 0 citations
It is shown that functional correctness alone is insufficient to assess the operational reliability of LLM-generated software before deployment in continuously running environments, and that memory usage is the most consistent indicator of potential software aging.
César Santos, Michele Vitagliano, Roberto Natella et al.· 0 citations
This work introduces Path2Spec, a divide-and-conquer framework that leverages LLMs to extract all execution paths from an input program, generates path-specific specifications for each, and merges them into a comprehensive overall specification.
Dan Huang, Zhensu Sun, Hui-Hui Huang et al.· 0 citations
Analysis of technique papers from ICSE 2026 finds that a newer model with a single prompt natively outperforms the heavily engineered tooling proposed just a year prior, and identifies that constructive techniques like code generation or repair are more amenable to substitution by a single-prompt.
Nahian Salsabil, Joy Saha, S. Dristi et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
