Enhancing Dynamic Symbolic Execution by Automatically Learning Search Heuristics

We present a technique to automatically generate search heuristics for dynamic symbolic execution. A key challenge in dynamic symbolic execution is how to effectively explore the program's execution paths to achieve high code coverage in a limited time budget. Dynamic symbolic execution employs...

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Published in:IEEE transactions on software engineering Vol. 48; no. 9; pp. 3640 - 3663
Main Authors: Cha, Sooyoung, Hong, Seongjoon, Bak, Jiseong, Kim, Jingyoung, Lee, Junhee, Oh, Hakjoo
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2022
IEEE Computer Society
Subjects:
Algorithms
concolic testing
Dynamic symbolic execution
execution-generated testing
Heuristic
Heuristic algorithms
Learning
Open source software
search heuristics
Search problems
Searching
Software algorithms
Software testing
Testing
ISSN:0098-5589, 1939-3520
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Abstract We present a technique to automatically generate search heuristics for dynamic symbolic execution. A key challenge in dynamic symbolic execution is how to effectively explore the program's execution paths to achieve high code coverage in a limited time budget. Dynamic symbolic execution employs a search heuristic to address this challenge, which favors exploring particular types of paths that are most likely to maximize the final coverage. However, manually designing a good search heuristic is nontrivial and typically ends up with suboptimal and unstable outcomes. The goal of this paper is to overcome this shortcoming of dynamic symbolic execution by automatically learning search heuristics. We define a class of search heuristics, namely a parametric search heuristic, and present an algorithm that efficiently finds an optimal heuristic for each subject program. Experimental results with industrial-strength symbolic execution tools (e.g., KLEE) show that our technique can successfully generate search heuristics that significantly outperform existing manually-crafted heuristics in terms of branch coverage and bug-finding.
AbstractList We present a technique to automatically generate search heuristics for dynamic symbolic execution. A key challenge in dynamic symbolic execution is how to effectively explore the program's execution paths to achieve high code coverage in a limited time budget. Dynamic symbolic execution employs a search heuristic to address this challenge, which favors exploring particular types of paths that are most likely to maximize the final coverage. However, manually designing a good search heuristic is nontrivial and typically ends up with suboptimal and unstable outcomes. The goal of this paper is to overcome this shortcoming of dynamic symbolic execution by automatically learning search heuristics. We define a class of search heuristics, namely a parametric search heuristic, and present an algorithm that efficiently finds an optimal heuristic for each subject program. Experimental results with industrial-strength symbolic execution tools (e.g., KLEE) show that our technique can successfully generate search heuristics that significantly outperform existing manually-crafted heuristics in terms of branch coverage and bug-finding.
Author Lee, Junhee
Cha, Sooyoung
Bak, Jiseong
Kim, Jingyoung
Oh, Hakjoo
Hong, Seongjoon
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Snippet We present a technique to automatically generate search heuristics for dynamic symbolic execution. A key challenge in dynamic symbolic execution is how to...
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SubjectTerms Algorithms
concolic testing
Dynamic symbolic execution
execution-generated testing
Heuristic
Heuristic algorithms
Learning
Open source software
search heuristics
Search problems
Searching
Software algorithms
Software testing
Testing
Title Enhancing Dynamic Symbolic Execution by Automatically Learning Search Heuristics
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