Year: 2025
Type: conference-paper
Abstract: Formal specifications are essential for reasoning about the correctness of complex systems. While recent advances have explored automatically learning such specifications, the challenge of distinguishing meaningful, non-trivial specifications from a vast and noisy pool of learned candidates remains largely open. In this position paper, we present an approach for specification ranking, aimed at identifying the most critical specifications that contribute to overall system correctness. To this end, we develop a four-metric rating framework that quantifies the importance of a specification. Our approach leverages the reasoning capabilities of Large Language Models to rank specifications from a set of automatically learned candidates. We evaluate the proposed method on a set of specifications inferred for 11 open-source and 3 proprietary distributed system benchmarks, demonstrating its effectiveness in ranking critical specifications.
Authors: Mike He, Zhendong Ang, Ankush Desai, Aarti Gupta
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4415002728