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Fixing Code that Explodes Under Symbolic Evaluation

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Verification, Model Checking, and Abstract Interpretation (VMCAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11990))

Abstract

Effective symbolic evaluation is key to building scalable verification and synthesis tools based on SMT solving. These tools use symbolic evaluators to reduce the semantics of all paths through a finite program to logical constraints, discharged with an SMT solver. Using an evaluator effectively requires tool developers to be able to identify and repair performance bottlenecks in code under all-path evaluation, a difficult task, even for experts. This paper presents a new method for repairing such bottlenecks automatically. The key idea is to formulate the symbolic performance repair problem as combinatorial search through a space of semantics-preserving transformations, or repairs, to find an equivalent program with minimal cost under symbolic evaluation. The key to realizing this idea is (1) defining a small set of generic repairs that can be combined to fix common bottlenecks, and (2) searching for combinations of these repairs to find good solutions quickly and best ones eventually. Our technique, SymFix, contributes repairs based on deforestation and symbolic reflection, and an efficient algorithm that uses symbolic profiling to guide the search for fixes. To evaluate SymFix, we implement it for the Rosette solver-aided language and symbolic evaluator. Applying SymFix to 18 published verification and synthesis tools built in Rosette, we find that it automatically improves the performance of 12 tools by a factor of 1.1 \(\times \)–91.7 \(\times \), and 4 of these fixes match or outperform expert-written repairs. SymFix also finds 5 fixes that were missed by experts.

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Authors and Affiliations

  1. University of Washington, Seattle, WA, 98195, USA

    Sorawee Porncharoenwase, James Bornholt & Emina Torlak

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  1. Sorawee Porncharoenwase
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Correspondence to Sorawee Porncharoenwase .

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  1. Ludwig-Maximilians-Universität München, Munich, Germany

    Dirk Beyer

  2. Max Planck Institute for Software Systems, Kaiserslautern, Rheinland-Pfalz, Germany

    Damien Zufferey

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Porncharoenwase, S., Bornholt, J., Torlak, E. (2020). Fixing Code that Explodes Under Symbolic Evaluation. In: Beyer, D., Zufferey, D. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2020. Lecture Notes in Computer Science(), vol 11990. Springer, Cham. https://doi.org/10.1007/978-3-030-39322-9_3

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  • DOI: https://doi.org/10.1007/978-3-030-39322-9_3

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