Aug 2026· Proceedings of the 2nd ACM SIGPLAN International Workshop on Language Models and Programming Languages· 2 citations· 39 references
Computer Science
TL;DR
FlowCheck, a constraint language to specify user-visible information flows directly through the application interface, where constraints can also be displayed and inspected without reading code, and are structured enough for reliable LLM generation.
Abstract
Vibe-coded applications often contain silent behavioral failures in which the interface appears functional even though user-visible information does not flow to the expected state or output. We introduce FlowCheck, a constraint language to specify these user-visible information flows directly through the application interface, where constraints can also be displayed and inspected without reading code, and are structured enough for reliable LLM generation. FlowCheck translates the constraints into deterministic CodeQL analyses, and we evaluate it across four applications generated via Claude Code, and compare with three coding models as bug-finding baselines. We find that FlowCheck correctly translates and flags all 30 of our injected constraint violations with no false positives. In contrast, frontier models (Claude Opus 4.7, DeepSeek V3, and Gemini Pro) showed significantly lower accuracy when prompted to find bugs in the same code, with none achieving full accuracy. This approach lets vibe coders state intent in terms of the interface they understand, and checks it deterministically against the code they do not.
SemBench is introduced, a novel benchmark consisting of 1000 diverse C programs sourced from the CodeParrot GitHub-code dataset, with 15,404 semantic questions spanning six basic but fundamental properties: dead code-statement, data dependency, function reachability, dominator, dead code-loop, and liveness.
Jade Xu, Ren-Liang Sun, Zijian Ding et al.· Communications AI & Computin...· 0 citations
The results show that executable feedback can repair secure-code generation, but its benefits depend on the model, task, feedback entry point, and especially test coverage.
Yun-Hao Liang, Cheng-Guang Gan, Rui-Xuan Ying et al.· 2 citations
The results show that point accuracy alone is insufficient for characterizing LLM reliability in assertion generation and motivate robustness-aware evaluation for AI-assisted hardware verification.
This work presents SpecRead, a benchmark that isolates specification comprehension from generation ability, and is automatically scorable by deterministic checks, with gray-zone cases counted wrong under the conservative main scoring.
Large language models (LLMs) may generate unreliable code on corner cases missed by testing, while formal verification can provide machine-checkable guarantees. Recently, researchers have proposed several benchmarks to evaluate the capabilities of LLMs in generating formally verifiable code, where LLMs need to formulat...
Jia-Ru Qian, Yihong Dong, Yong-Ming Li et al.· 0 citations
Static taint analyzers are widely used in software security, yet aggregate vulnerability-detection metrics provide limited insight into the specific language constructs and analysis capabilities that cause tools to succeed or fail. This paper presents xAST, a fine-grained benchmark containing 844 paired capability case...
Yan-Jie Zhao, Shen-Ao Wang, Jian Zhao et al.· ACM Transactions on Software...· 0 citations
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