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research-article

Learning to Explore Paths for Symbolic Execution

Authors:

Gishor Sivanrupan,

Martin VechevAuthors Info & Claims

CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

Pages 2526 - 2540

https://doi.org/10.1145/3460120.3484813

Published: 13 November 2021 Publication History

Abstract

Symbolic execution is a powerful technique that can generate tests steering program execution into desired paths. However, the scalability of symbolic execution is often limited by path explosion, i.e., the number of symbolic states representing the paths under exploration quickly explodes as execution goes on. Therefore, the effectiveness of symbolic execution engines hinges on the ability to select and explore the right symbolic states.

In this work, we propose a novel learning-based strategy, called Learch, able to effectively select promising states for symbolic execution to tackle the path explosion problem. Learch directly estimates the contribution of each state towards the goal of maximizing coverage within a time budget, as opposed to relying on manually crafted heuristics based on simple statistics as a crude proxy for the objective. Moreover, Learch leverages existing heuristics in training data generation and feature extraction, and can thus benefit from any new expert-designed heuristics. We instantiated Learch in KLEE, a widely adopted symbolic execution engine. We evaluated Learch on a diverse set of programs, showing that Learch is practically effective: it covers more code and detects more security violations than existing manual heuristics, as well as combinations of those heuristics. We also show that using tests generated by Learch as initial fuzzing seeds enables the popular fuzzer AFL to find more paths and security violations.

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Cited By

Dong YWang SZhang LLiu XLiu S(2025)Automatic detection of infeasible paths in large-scale program based on program summariesScience of Computer Programming10.1016/j.scico.2024.103183239:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.scico.2024.103183
He LZhang DZhu DZhang JWang Rliu J(2024)Path Exploration Strategy for Symbolic Execution based on Multi-strategy Active LearningProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3671403(165-168)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3671403
Yoon JCha S(2024)FeatMaker: Automated Feature Engineering for Search Strategy of Symbolic ExecutionProceedings of the ACM on Software Engineering10.1145/36608151:FSE(2447-2468)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660815
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Index Terms

Learning to Explore Paths for Symbolic Execution
1. Security and privacy
  1. Software and application security
    1. Software security engineering
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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cover image ACM Conferences

CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security

November 2021

3558 pages

ISBN:9781450384544

DOI:10.1145/3460120

General Chairs:
Yongdae Kim
KAIST, Republic of Korea
,
Jong Kim
POSTECH, Republic of Korea
,
Program Chairs:
Giovanni Vigna
University of California, Santa Barbara / VMware, USA
,
Elaine Shi
Carnegie Mellon University, USA

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Cited By

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https://dl.acm.org/doi/10.1016/j.scico.2024.103183
He LZhang DZhu DZhang JWang Rliu J(2024)Path Exploration Strategy for Symbolic Execution based on Multi-strategy Active LearningProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3671403(165-168)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3671403
Yoon JCha S(2024)FeatMaker: Automated Feature Engineering for Search Strategy of Symbolic ExecutionProceedings of the ACM on Software Engineering10.1145/36608151:FSE(2447-2468)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660815
Yi QYu YYang G(2024)Compatible Branch Coverage Driven Symbolic Execution for Efficient Bug FindingProceedings of the ACM on Programming Languages10.1145/36564438:PLDI(1633-1655)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3656443
Wang HTang ZTan SWang JLiu YFang HXia CWang ZRoychoudhury APaiva AAbreu RStorey M(2024)Combining Structured Static Code Information and Dynamic Symbolic Traces for Software Vulnerability PredictionProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639212(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639212
Sun YYang GLv SLi ZSun LRoychoudhury APaiva AAbreu RStorey M(2024)Concrete Constraint Guided Symbolic ExecutionProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639078(1-12)Online publication date: 20-May-2024
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Hu JDuan YYin HRoychoudhury APaiva AAbreu RStorey M(2024)Marco: A Stochastic Asynchronous Concolic ExplorerProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3623301(1-12)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3623301
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