Subformula Caching for Model Counting and Quantitative Program Analysis
| Autor: | William Eiers, Tevfik Bultan, Tegan Brennan, Seemanta Saha |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Probabilistic logic 020207 software engineering 02 engineering and technology Symbolic execution computer.software_genre Software quality Bottleneck Program analysis Scalability 0202 electrical engineering electronic engineering information engineering Data mining computer Constraint satisfaction problem |
| Zdroj: | ASE |
| Popis: | Quantitative program analysis is an emerging area with applications to software reliability, quantitative information flow, side-channel detection and attack synthesis. Most quantitative program analysis techniques rely on model counting constraint solvers, which are typically the bottleneck for scalability. Although the effectiveness of formula caching in expediting expensive model-counting queries has been demonstrated in prior work, our key insight is that many subformulas are shared across non-identical constraints generated during program analyses. This has not been utilized by prior formula caching approaches. In this paper we present a subformula caching framework and integrate it into a model counting constraint solver. We experimentally evaluate its effectiveness under three quantitative program analysis scenarios: 1) model counting constraints generated by symbolic execution, 2) reliability analysis using probabilistic symbolic execution, 3) adaptive attack synthesis for side-channels. Our experimental results demonstrate that our subformula caching approach significantly improves the performance of quantitative program analysis. |
| Databáze: | OpenAIRE |
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