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Deducing Matching Strings for Real-World Regular Expressions

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Dependable Software Engineering. Theories, Tools, and Applications (SETTA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14464))

Abstract

Real-world regular expressions (regexes for short) have a wide range of applications in software. However, the support for regexes in test generation is insufficient. For example, existing works lack support for some important features such as lookbehind, are not resilient to subtle semantic differences (such as partial/full matching), fall short of Unicode support, leading to loss of test coverage or missed bugs. To address these challenges, in this paper, we propose a novel semantic model for comprehensively modeling the extended features in regexes, with an awareness of different matching semantics (i.e. partial/full matching) and matching precedence (i.e. greedy/lazy matching). To the best of our knowledge, this is the first attempt to consider partial/full matching semantics in modeling and to support lookbehind. Leveraging this model we then develop PowerGen, a tool for deducing matching strings for regexes, which randomly generates matching strings from the input regex effectively. We evaluate PowerGen against nine related state-of-the-art tools. The evaluation results show the high effectiveness and efficiency of PowerGen.

Y. Yan and W. Su—These authors contributed equally.

Zixuan Chen is currently employed at Kuaishou Technology.

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Notes

  1. 1.

    To facilitate error identification, we simplify lengthy regexes by isolating the problematic fragment.

  2. 2.

    https://cdn.jsdelivr.net/npm/dataset2023/.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their helpful comments and suggestions. Work supported by the Natural Science Foundation of Beijing, China (Grant No. 4232038) and the National Natural Science Foundation of China (Grant No. 62372439).

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Authors and Affiliations

  1. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China

    Yixuan Yan, Weihao Su, Mengxi Wang, Haiming Chen, Chengyao Peng, Rongchen Li & Zixuan Chen

  2. University of Chinese Academy of Sciences, Beijing, 101400, China

    Yixuan Yan, Weihao Su, Mengxi Wang, Haiming Chen, Chengyao Peng, Rongchen Li & Zixuan Chen

  3. College of Computer Science and Technology, Huaqiao University, Xiamen, China

    Lixiao Zheng

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Correspondence to Haiming Chen .

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  1. Saarland University, Saarbrücken, Germany

    Holger Hermanns

  2. Singapore Management University, Singapore, Singapore

    Jun Sun

  3. Nanjing University, Nanjing, China

    Lei Bu

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Yan, Y. et al. (2024). Deducing Matching Strings for Real-World Regular Expressions. In: Hermanns, H., Sun, J., Bu, L. (eds) Dependable Software Engineering. Theories, Tools, and Applications. SETTA 2023. Lecture Notes in Computer Science, vol 14464. Springer, Singapore. https://doi.org/10.1007/978-981-99-8664-4_19

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