TL;DR
An approach to verifying the results of static code analysis using large language models (LLMs), which filters warnings to eliminate false positives, which was implemented in SharpChecker, an industrial static analyzer for C#.
An approach to improving the recall of static analysis of .NET applications using frameworks, such as WPF and Entity Framework Core, is proposed, based on generating an equivalent C# code that models user interaction scenarios that significantly improves the recall of static analysis of framework-based applications.
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.
This work studies an internal signal of code correctness that is able to judge candidate solutions better than the model's token-level or stated confidence, leaving open an important question: whether it reflects a robust property of the model or an artifact of that choice.
Investigating Behavior Trees (BTs) as an alternative intermediate representation for LLM-based vulnerability detection suggests that BTs can provide a compact and useful structured representation for vulnerability detection with quantized, locally deployable LLMs.
Eiffel-tools, a language server protocol (LSP) implementation for the Eiffel programming language that uses Large Language Models (LLMs) to aid the development of statically verified software, is introduced.
It is observed that generated code often omits basic input validation or memory-safety checks, which can lead to overflows, resource exhaustion, or other reliability/security issues, and even the largest models frequently make simple mistakes.
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