16802

Implementing and Evaluating Candidate-Based Invariant Generation

Adam Betts, Nathan Chong, Pantazis Deligiannis, Alastair F. Donaldson, Jeroen Ketema
Department of Computing, Imperial College London, London SW7 2AZ, UK
arXiv:1612.01198 [cs.SE], (4 Dec 2016)

@article{betts2016implementing,

   title={Implementing and Evaluating Candidate-Based Invariant Generation},

   author={Betts, Adam and Chong, Nathan and Deligiannis, Pantazis and Donaldson, Alastair F. and Ketema, Jeroen},

   year={2016},

   month={dec},

   archivePrefix={"arXiv"},

   primaryClass={cs.SE}

}

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The discovery of inductive invariants lies at the heart of static program verification. This paper describes our efforts to apply candidate-based invariant generation in GPUVerify, a static checker of programs that run on GPUs. We study a set of 383 GPU programs that contain loops, drawn from a number of open source suites and vendor SDKs. Among this set, 253 benchmarks require provision of loop invariants for verification to succeed. We describe the methodology we used to incrementally improve the invariant generation capabilities of GPUVerify to handle these benchmarks, through candidate-based invariant generation. We also describe a set of experiments that we used to examine the effectiveness of our rules for candidate generation, assessing rules based on their generality (the extent to which they generate candidate invariants), hit rate (the extent to which the generated candidates hold), effectiveness (the extent to which provable candidates actually help in allowing verification to succeed), and influence (the extent to which the success of one generation rule depends on candidates generated by another rule). We believe that our methodology for devising and evaluation candidate generation rules may serve as a useful framework for other researchers interested in candidate-based invariant generation. The candidates produced by GPUVerify help to verify 231 of these 253 programs. An increase in precision, however, has created sluggishness in GPUVerify because more time is spent on computing inductive invariants. To speed up this process, we have investigated four under-approximating program analyses that aim to reject false candidates quickly and a framework whereby these analyses can run in sequence or in parallel. We examine the speedups associated with our under-approximating analyses across two platforms, running Windows and Linux.
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