Bibliographic Details
| Title: |
Unifying Model Execution and Deductive Verification with Interaction Trees in Isabelle/HOL. |
| Authors: |
Foster, Simon, Hur, Chung-Kil, Woodcock, Jim |
| Source: |
ACM Transactions on Software Engineering & Methodology; May2025, Vol. 34 Issue 4, p1-40, 40p |
| Subject Terms: |
SOFTWARE engineering, SEMANTICS, LOGIC, MATHEMATICAL proofs, SIMULATION methods & models, PARALLEL programs (Computer programs), INTERACTIVE computer systems |
| Abstract: |
Model execution allows us to prototype and analyse software engineering models by stepping through their possible behaviours, using techniques like animation and simulation. On the other hand, deductive verification allows us to construct formal proofs demonstrating satisfaction of certain critical properties in support of high-assurance software engineering. To ensure coherent results between execution and proof, we need unifying semantics and automation. In this article, we mechanise Interaction Trees (ITrees) in Isabelle/HOL to produce an execution and verification framework. ITrees are coinductive structures that allow us to encode infinite labelled transition systems, yet they are inherently executable. We use ITrees to create verification tools for stateful imperative programs, concurrent programs with message passing in the form of the CSP and Circus languages, and abstract system models in the style of the Z and B methods. We demonstrate how ITrees can account for diverse semantic presentations, such as structural operational semantics, a relational program model, and CSP's failures-divergences trace model. Finally, we demonstrate how ITrees can be executed using the Isabelle code generator to support the animation of models. [ABSTRACT FROM AUTHOR] |
|
Copyright of ACM Transactions on Software Engineering & Methodology is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |