arXiv AI By Wen Fan, Minh Tran, Sanya Dod, Xin Hu, Marilyn Rego, Danning Xie, Jenna DiVincenzo, Lin Tan

An Empirical Study of LLM-Generated Specifications for VeriFast

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arXiv:2606. 26490v1 Announce Type: cross Abstract: Static verification tools can assure industrial scale software, but require significant human labor to write specifications.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
3d ago

Self-Spec Verifiable Code Generation

arXiv:2609.39568v1 Announce Type: cross Abstract: Large language models (LLMs) may generate unreliable code on corner cases missed by testing, while formal verification can provide machine-checkable...

By Jiaru Qian, Yihong Dong, Yongmin Li, Hao Zhu, Bin Gu, Ge Li
arXiv AI
Sep 11

Spec-Harness: Measuring and Improving Behavioral Adequacy of LLM-Synthesized Formal Specifications

Spec‑Harness evaluates how well large language models (LLMs) synthesize Java Modeling Language (JML) specifications by measuring behavioral adequacy across precondition and postcondition correctness and completeness. The study shows that while prompt optimization can raise verifier pass rates, many accepted specifications remain behaviorally weak, either over‑ or under‑constraining inputs and outputs. Spec‑Harness also serves as a feedback mechanism that improves the quality of specifications generated by general‑purpose coding agents and a specialized JML agent.

By Md Rakib Hossain Misu, Iris Ma, Cristina V. Lopes