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Towards Efficient Data-Flow Test Data Generation

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Theories of Programming and Formal Methods

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14080))

Abstract

Data-flow testing (DFT) aims to detect potential data interaction anomalies by focusing on the points at which variables receive values and the points at which these values are used. Such test objectives are referred as def-use pairs. However, the complexity of DFT still overwhelms the testers in practice. To tackle this problem, we introduce a hybrid testing framework for data-flow based test generation: (1) The core of our framework is symbolic execution (SE), enhanced by a novel guided path exploration strategy to improve testing performance; and (2) we systematically cast DFT as reachability checking in software model checking (SMC) to complement SE, yielding practical DFT that combines the two techniques’ strengths. We implemented our framework for C programs on top of the state-of-the-art symbolic execution engine KLEE and instantiated with three different software model checkers. Our evaluation on the 28,354 def-use pairs collected from 33 open-source and industrial program subjects shows that (1) our SE-based approach can improve DFT performance by 15–48% in terms of testing time, compared with existing search strategies; and (2) our combined approach can further reduce testing time by 20.1–93.6%, and improve data-flow coverage by 27.8–45.2% by eliminating infeasible test objectives. This combined approach also enables the cross-checking of each component for reliable and robust testing results.

This paper was completed on Jifeng He’s 80th birthday, May 2023.

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Notes

  1. 1.

    In this paper, we focus on the problem of classic data-flow testing [39, 89], i.e., finding an input for a given def-use pair at one time. We do not consider the case where some pairs can be accidentally covered when targeting one pair, since this has already been investigated in collateral coverage-based approach [65, 66].

  2. 2.

    We follow the all def-use coverage defined by Rapps and Weyuker [75, 76], since almost all of the literature that followed uses or extends this definition, as revealed by a recent survey [86].

  3. 3.

    An pending path indicates a not fully-explored path (corresponding to an unterminated state).

  4. 4.

    We use the line number followed by T or F to denote the true or false branch of the if statement at the corresponding line.

  5. 5.

    We use CIL as the C parser to transform the source code into an equivalent simplified form using the –dosimplify option, where one statement contains at most one operator.

  6. 6.

    We use the latest versions of these model checkers at the time of writing.

  7. 7.

    Cyclomatic complexity is a software metric that indicates the complexity of a program. The standard software development guidelines recommend the cyclomatic complexity of a module should not exceeded 10.

  8. 8.

    SIQR = (Q3-Q1)/2, which measures the variability of testing time, where Q1 is the lower quartile, and Q3 is the upper quartile.

  9. 9.

    In theory, the symbolic execution-based approach cannot identify infeasible pairs unless it enumerates all possible paths, which however is impossible in practice. Therefore, we only consider the testing times of covered (feasible) pairs for performance evaluation.

  10. 10.

    BLAST hangs on totinfo, and CPAchecker crashes on parts of pairs from cdaudio.

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Acknowledgements

This work is in honor of Jifeng He’s contribution to computer science, especially establishing the Unifying Theories of Programming (UTP). This work applies formal methods to support software testing, which was influenced by the work of Jifeng He. Ting Su, the lead author of this work, sincerely appreciate the academic guidance from his PhD supervisor Jifeng He.

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Authors and Affiliations

  1. East China Normal University, Shanghai, China

    Ting Su & Yichen Yan

  2. ETH Zurich, Zürich, Switzerland

    Chengyu Zhang & Zhendong Su

  3. Nankai University, Tianjin, China

    Lingling Fan

  4. Nanyang Technological University, Singapore, Singapore

    Yang Liu

  5. State University of New York, Incheon, Korea

    Zhoulai Fu

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  6. Zhoulai Fu
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  7. Zhendong Su
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Correspondence to Ting Su .

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  1. London South Bank University, London, UK

    Jonathan P. Bowen

  2. East China Normal University, Shanghai, China

    Qin Li

  3. University of Macau, Macau, China

    Qiwen Xu

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Su, T. et al. (2023). Towards Efficient Data-Flow Test Data Generation. In: Bowen, J.P., Li, Q., Xu, Q. (eds) Theories of Programming and Formal Methods. Lecture Notes in Computer Science, vol 14080. Springer, Cham. https://doi.org/10.1007/978-3-031-40436-8_10

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  • DOI: https://doi.org/10.1007/978-3-031-40436-8_10

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