Sound and complete witnesses for template-based verification of LTL properties on polynomial programs

Chatterjee K, Goharshady AK, Goharshady E, Karrabi M, Zikelic D. 2024. Sound and complete witnesses for template-based verification of LTL properties on polynomial programs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). FM: Formal Methods, LNCS, vol. 14933, 600–619.

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Department
Series Title
LNCS
Abstract
We study the classical problem of verifying programs with respect to formal specifications given in the linear temporal logic (LTL). We first present novel sound and complete witnesses for LTL verification over imperative programs. Our witnesses are applicable to both verification (proving) and refutation (finding bugs) settings. We then consider LTL formulas in which atomic propositions can be polynomial constraints and turn our focus to polynomial arithmetic programs, i.e. programs in which every assignment and guard consists only of polynomial expressions. For this setting, we provide an efficient algorithm to automatically synthesize such LTL witnesses. Our synthesis procedure is both sound and semi-complete. Finally, we present experimental results demonstrating the effectiveness of our approach and that it can handle programs which were beyond the reach of previous state-of-the-art tools.
Publishing Year
Date Published
2024-09-11
Proceedings Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer Nature
Acknowledgement
This work was supported in part by the ERC-2020-CoG 863818 (FoRM-SMArt) and the Hong Kong Research Grants Council ECS Project Number 26208122.
Volume
14933
Page
600-619
Conference
FM: Formal Methods
Conference Location
Milan, Italy
Conference Date
2024-09-09 – 2024-09-13
ISSN
eISSN
IST-REx-ID

Cite this

Chatterjee K, Goharshady AK, Goharshady E, Karrabi M, Zikelic D. Sound and complete witnesses for template-based verification of LTL properties on polynomial programs. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 14933. Springer Nature; 2024:600-619. doi:10.1007/978-3-031-71162-6_31
Chatterjee, K., Goharshady, A. K., Goharshady, E., Karrabi, M., & Zikelic, D. (2024). Sound and complete witnesses for template-based verification of LTL properties on polynomial programs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 14933, pp. 600–619). Milan, Italy: Springer Nature. https://doi.org/10.1007/978-3-031-71162-6_31
Chatterjee, Krishnendu, Amir Kafshdar Goharshady, Ehsan Goharshady, Mehrdad Karrabi, and Dorde Zikelic. “Sound and Complete Witnesses for Template-Based Verification of LTL Properties on Polynomial Programs.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14933:600–619. Springer Nature, 2024. https://doi.org/10.1007/978-3-031-71162-6_31.
K. Chatterjee, A. K. Goharshady, E. Goharshady, M. Karrabi, and D. Zikelic, “Sound and complete witnesses for template-based verification of LTL properties on polynomial programs,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Milan, Italy, 2024, vol. 14933, pp. 600–619.
Chatterjee K, Goharshady AK, Goharshady E, Karrabi M, Zikelic D. 2024. Sound and complete witnesses for template-based verification of LTL properties on polynomial programs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). FM: Formal Methods, LNCS, vol. 14933, 600–619.
Chatterjee, Krishnendu, et al. “Sound and Complete Witnesses for Template-Based Verification of LTL Properties on Polynomial Programs.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14933, Springer Nature, 2024, pp. 600–19, doi:10.1007/978-3-031-71162-6_31.
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