Conflict-Directed Graph Coverage

Schwartz-Narbonne, Daniel; Schäf, Martin; Jovanović, Dejan; Rümmer, Philipp; Wies, Thomas

doi:10.1007/978-3-319-17524-9_23

Cited by 3 publications

(1 citation statement)

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“…An orthogonal approach that can help reduce the number of paths to check is presented in [90]. While an SMT solver can be used to explore a large search space one path at a time, it will often end up reasoning over control flows shared by many paths.…”

Section: Pruning Unrealizable Pathsmentioning

confidence: 99%

A Survey of Symbolic Execution Techniques

et al. 2018

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Many security and software testing applications require checking whether certain properties of a program hold for any possible usage scenario. For instance, a tool for identifying software vulnerabilities may need to rule out the existence of any backdoor to bypass a program's authentication. One approach would be to test the program using different, possibly random inputs. As the backdoor may only be hit for very specific program workloads, automated exploration of the space of possible inputs is of the essence. Symbolic execution provides an elegant solution to the problem, by systematically exploring many possible execution paths at the same time without necessarily requiring concrete inputs. Rather than taking on fully specified input values, the technique abstractly represents them as symbols, resorting to constraint solvers to construct actual instances that would cause property violations. Symbolic execution has been incubated in dozens of tools developed over the last four decades, leading to major practical breakthroughs in a number of prominent software reliability applications. The goal of this survey is to provide an overview of the main ideas, challenges, and solutions developed in the area, distilling them for a broad audience.If you are considering citing this survey, we would appreciate if you could use the following BibTeX entry: http://goo.gl/Hf5Fvc 0:2 R. Baldoni et al.

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Section: Pruning Unrealizable Pathsmentioning

confidence: 99%