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Scalable Linear Invariant Generation with Farkas' Lemma


Invariant generation is the classical problem to automatically generate invariants to aid the formal analysis of programs. In this work, we consider the linear-invariant-generation problem over affine programs (i.e., programs with affine guards and updates). In the literature, the only known sound and complete characterization to solve this problem is via Farkas' Lemma (FL), and has been implemented through either quantifier elimination or reasonable heuristics. Although FL-based approaches can generate highly accurate linear invariants from the completeness of FL, the main bottleneck to apply these approaches is the scalability issue caused by either non-linear constraints or combinatorial explosion. It has long been an unresolved problem to improve the scalability of FL-based approaches. In this work, we address this issue with novel algorithmic improvements. In detail, we base our approach on the only practical FL-based approach [Sankaranarayanan et al., SAS 2004] that applies FL with reasonable heuristics, and develop two novel and independent improvements to leverage the scalability. The first improvement is the novel idea to generate invariants at only one program location in a single invariant-generation process, so that the invariants for each location are generated separately rather than together in a single computation. This idea naturally leads to a parallel processing that divides the invariant-generation task for all program locations by assigning the locations separately to mulitple processors. Moreover, the idea enables us to develop detailed technical improvements to further reduce the combinatorial explosion in the original work [Sankaranarayanan et al., SAS 2004]. The second improvement is a segmented subsumption testing in the CNF-to-DNF expansion that allows to discover more local subsumptions in advance. We formally prove that our approach has the same accuracy as the original work, thus does not incur accuracy loss on the generated invariants. Moreover, experimental results on representative benchmarks involving non-trivial linear invariants demonstrate that our approach improves the runtime of the original work by several orders of magnitude, even if in the non-parallel scenario that sums up the execution time for all program locations. Thus, our approach constitutes the first significant improvement in FL-based approaches for linear invariant generation after almost two decades.
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Dates and versions

hal-03463338 , version 1 (02-12-2021)
hal-03463338 , version 2 (22-03-2022)


  • HAL Id : hal-03463338 , version 2


Hongming Liu, Hongfei Fu, Zhiyong Yu, Jiaxin Song, Guoqiang Li. Scalable Linear Invariant Generation with Farkas' Lemma. 2022. ⟨hal-03463338v2⟩
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