Program verification offers a framework for ensuring program correctness and
therefore systematically eliminating different classes of bugs. Inferring loop invariants is one of the main challenges behind automated verification of real-world
programs which often contain many loops. In this paper, we present Continuous Logic Network (CLN), a novel neural architecture for automatically learning
loop invariants directly from program execution traces. Unlike existing neural networks, CLNs can learn precise and explicit representations of formulas in Satisfiability Modulo Theories (SMT) for loop invariants from program execution traces.
We develop a new sound and complete semantic mapping for assigning SMT formulas to continuous truth values that allows CLNs to be trained efficiently. We use
CLNs to implement a new inference system for loop invariants, CLN2INV, that
significantly outperforms existing approaches on the popular Code2Inv dataset.
CLN2INV is the first tool to solve all 124 theoretically solvable problems in the
Code2Inv dataset. Moreover, CLN2INV takes only 1.1 seconds on average for
each problem, which is 40× faster than existing approaches. We further demonstrate that CLN2INV can even learn 12 significantly more complex loop invariants
than the ones required for the Code2Inv dataset.
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This content will become publicly available on January 5, 2025
Solvable Polynomial Ideals: The Ideal Reflection for Program Analysis
This paper presents a program analysis method that generates program summaries involving polynomial arithmetic. Our approach builds on prior techniques that use solvable polynomial maps for summarizing loops. These techniques are able to generate all polynomial invariants for a restricted class of programs, but cannot be applied to programs outside of this class---for instance, programs with nested loops, conditional branching, unstructured control flow, etc. There currently lacks approaches to apply these prior methods to the case of general programs. This paper bridges that gap. Instead of restricting the kinds of programs we can handle, our method abstracts every loop into a model that can be solved with prior techniques, bringing to bear prior work on solvable polynomial maps to general programs. While no method can generate all polynomial invariants for arbitrary programs, our method establishes its merit through a monotonicty result. We have implemented our techniques, and tested them on a suite of benchmarks from the literature. Our experiments indicate our techniques show promise on challenging verification tasks requiring non-linear reasoning.
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- Award ID(s):
- 1942537
- PAR ID:
- 10501908
- Publisher / Repository:
- Association for Computing Machinery
- Date Published:
- Journal Name:
- Proceedings of the ACM on Programming Languages
- Volume:
- 8
- Issue:
- POPL
- ISSN:
- 2475-1421
- Page Range / eLocation ID:
- 724 to 752
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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