GenCorres: Consistent Shape Matching via Coupled Implicit-Explicit Shape Generative Models.
- Award ID(s):
- 2047677
- PAR ID:
- 10570937
- Publisher / Repository:
- International Conference on Machine Learning
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Memory safety is a fundamental correctness property of software. For programs that manipulate linked, heap-allocated data structures, ensuring memory safety requires analyzing their possible shapes. Despite significant advances in shape analysis, existing techniques rely on hand-crafted domains tailored to specific data structures, making them difficult to generalize and extend. This paper presents a novel approach that reduces memory-safety proofs to the verification of heap-less imperative programs, enabling the use of off-the-shelf software verification tools. We achieve this reduction through two complementary program instrumentation techniques: space invariants, which enable symbolic reasoning about unbounded heaps, and flow abstraction, which encodes global heap properties as local flow equations. The approach effectively verifies memory safety across a broad range of programs, including concurrent lists and trees that lie beyond the reach of existing shape analysis tools.more » « less
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