Compositional Symbolic Execution for the Next 700 Memory Models (Extended Version)
Andreas L\"o\"ow, Seung Hoon Park, Daniele Nantes-Sobrinho, Sacha-\'Elie Ayoun, Opale Sj\"ostedt, Philippa Gardner

TL;DR
This paper develops a formal, mechanized foundation for compositional symbolic execution platforms that are parametric on memory models, enabling flexible analysis across diverse programming languages and memory systems.
Contribution
It introduces a new formal foundation for memory-model-parametric CSE platforms, validated across multiple models, and supports both SL and ISL analyses based on standard definitions.
Findings
Mechanized foundation in Rocq theorem prover
Validated across C and CHERI memory models
Supports both SL and ISL analyses
Abstract
Multiple successful compositional symbolic execution (CSE) tools and platforms exploit separation logic (SL) for compositional verification and/or incorrectness separation logic (ISL) for compositional bug-finding, including VeriFast, Viper, Gillian, CN, and Infer-Pulse. Previous work on the Gillian platform, the only CSE platform that is parametric on the memory model, meaning that it can be instantiated to different memory models, suggests that the ability to use custom memory models allows for more flexibility in supporting analysis of a wide range of programming languages, for implementing custom automation, and for improving performance. However, the literature lacks a satisfactory formal foundation for memory-model-parametric CSE platforms. In this paper, inspired by Gillian, we provide a new formal foundation for memory-model-parametric CSE platforms. Our foundation advances…
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