2013USENIX Annual Technical ConferenceRequires access

Redundant state detection for dynamic symbolic execution

Suhabe Bugrara, Dawson Engler

Open publisher page 23 citations

Abstract

Many recent tools use dynamic symbolic execution to perform tasks ranging from automatic test generation, finding security flaws, equivalence verification, and exploit generation. However, while symbolic execution is promising, it perennially struggles with the fact that the number of paths in a program increases roughly exponentially with both code and input size. This paper presents a technique that attacks this problem by eliminating paths that cannot reach new code before they are executed and evaluates it on 66 system intensive, complicated, and widely-used programs. Our experiments demonstrate that the analysis speeds up dynamic symbolic execution by an average of 50.5×, with a median of 10×, and increases coverage by an average of 3.8%.

About this research paper

What this paper is about

Many recent tools use dynamic symbolic execution to perform tasks ranging from automatic test generation, finding security flaws, equivalence verification, and exploit generation. However, while symbolic execution is promising, it perennially struggles with the fact that the number of paths in a program increases roughly exponentially with both code and input size. This paper presents a technique that attacks this problem by eliminating paths that cannot reach new code before they are executed and evaluates it on 66 system intensive, complicated, and widely-used programs. Our experiments demonstrate that the analysis speeds up dynamic symbolic execution by an average of 50.5×, with a median of 10×, and increases coverage by an average of 3.8%.

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OpenAlex reports 23 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Many recent tools use dynamic symbolic execution to perform tasks ranging from automatic test generation, finding security flaws, equivalence verification, and exploit generation. However, while symbolic execution is promising, it perennially struggles with the fact that the number of paths in a program increases roughly exponentially with both code and input size. This paper presents a technique that attacks this problem by eliminating paths that cannot reach new code before they are executed and evaluates it on 66 system intensive, complicated, and widely-used programs. Our experiments demonstrate that the analysis speeds up dynamic symbolic execution by an average of 50.5×, with a median of 10×, and increases coverage by an average of 3.8%.

Key concepts: Symbolic execution, Computer science, Concolic testing, Exploit, Equivalence (formal languages), Static analysis, Program analysis, Parallel computing

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