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White-box validation of quantitative product lines by statistical model checking and process mining (2401.13019v1)

Published 23 Jan 2024 in cs.SE

Abstract: We propose a novel methodology for validating software product line (PL) models by integrating Statistical Model Checking (SMC) with Process Mining (PM). Our approach focuses on the feature-oriented language QFLan in the PL engineering domain, allowing modeling of PLs with rich cross-tree and quantitative constraints, as well as aspects of dynamic PLs like staged configurations. This richness leads to models with infinite state-space, requiring simulation-based analysis techniques like SMC. For instance, we illustrate with a running example involving infinite state space. SMC involves generating samples of system dynamics to estimate properties such as event probabilities or expected values. On the other hand, PM uses data-driven techniques on execution logs to identify and reason about the underlying execution process. In this paper, we propose, for the first time, applying PM techniques to SMC simulations' byproducts to enhance the utility of SMC analyses. Typically, when SMC results are unexpected, modelers must determine whether they stem from actual system characteristics or model bugs in a black-box manner. We improve on this by using PM to provide a white-box perspective on the observed system dynamics. Samples from SMC are fed into PM tools, producing a compact graphical representation of observed dynamics. The mined PM model is then transformed into a QFLan model, accessible to PL engineers. Using two well-known PL models, we demonstrate the effectiveness and scalability of our methodology in pinpointing issues and suggesting fixes. Additionally, we show its generality by applying it to the security domain.

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References (44)
  1. H. L. Younes, Probabilistic verification for “black-box” systems, in: CAV 2015, Springer, 2005, pp. 253–265.
  2. doi:10.1007/978-3-642-32759-9_36.
  3. doi:10.1145/1806799.1806850.
  4. doi:10.1145/2593489.2593493.
  5. doi:10.4204/EPTCS.182.5.
  6. doi:10.1007/s10270-015-0475-z.
  7. doi:10.1007/978-3-662-54494-5_23.
  8. doi:10.1007/978-3-540-70545-1_16. URL https://doi.org/10.1007/978-3-540-70545-1_16
  9. doi:10.1007/s10664-020-09930-8. URL https://doi.org/10.1007/s10664-020-09930-8
  10. doi:https://doi.org/10.1016/S0167-6423(00)00018-6. URL https://www.sciencedirect.com/science/article/pii/S0167642300000186
  11. doi:10.1145/2791060.2791087. URL https://doi.org/10.1145/2791060.2791087
  12. doi:10.1007/978-3-540-28630-1_17.
  13. doi:10.1145/2556624.2556627.
  14. doi:10.1007/978-3-031-19759-8_21. URL https://doi.org/10.1007/978-3-031-19759-8_21
  15. arXiv:1905.06169. URL http://arxiv.org/abs/1905.06169
  16. doi:10.1109/ICSE.2013.6606593. URL https://doi.org/10.1109/ICSE.2013.6606593
  17. doi:10.1007/s00165-017-0432-4. URL https://doi.org/10.1007/s00165-017-0432-4
  18. doi:10.1016/j.scico.2013.09.019. URL https://doi.org/10.1016/j.scico.2013.09.019
  19. doi:10.1109/ICSE.2013.6606594. URL https://doi.org/10.1109/ICSE.2013.6606594
  20. doi:10.1145/1985793.1985838.
  21. doi:10.1145/2950290.2950318.
  22. doi:10.1145/2970276.2970322.
  23. doi:10.1109/COMPSAC.2012.95.
  24. doi:10.1007/978-3-662-46734-3_5. URL https://doi.org/10.1007/978-3-662-46734-3_5
  25. doi:10.1145/1985793.1985838. URL https://doi.org/10.1145/1985793.1985838
  26. doi:10.1007/978-3-642-36249-1_1. URL https://doi.org/10.1007/978-3-642-36249-1_1
  27. doi:10.1145/1147249.1147254. URL https://doi.org/10.1145/1147249.1147254
  28. doi:10.1016/j.jlamp.2015.11.006. URL https://doi.org/10.1016/j.jlamp.2015.11.006
  29. doi:10.1007/978-3-642-34026-0_11. URL https://doi.org/10.1007/978-3-642-34026-0_11
  30. doi:10.1145/2063239.2063245. URL https://doi.org/10.1145/2063239.2063245
  31. doi:10.1016/j.jlamp.2015.09.004. URL https://doi.org/10.1016/j.jlamp.2015.09.004
  32. doi:10.1145/1868294.1868298. URL https://doi.org/10.1145/1868294.1868298
  33. doi:10.1145/2648511.2648520. URL https://doi.org/10.1145/2648511.2648520
  34. doi:10.1007/s10009-012-0234-1. URL https://doi.org/10.1007/s10009-012-0234-1
  35. doi:10.1145/2499777.2499781. URL https://doi.org/10.1145/2499777.2499781
  36. doi:10.1007/978-3-642-32759-9_36. URL https://doi.org/10.1007/978-3-642-32759-9_36
  37. doi:10.1145/2647908.2655969. URL https://doi.org/10.1145/2647908.2655969
  38. doi:10.1007/978-3-030-16722-6_11. URL https://doi.org/10.1007/978-3-030-16722-6_11
  39. doi:10.1007/s10009-016-0425-2. URL https://doi.org/10.1007/s10009-016-0425-2
  40. doi:10.1007/978-3-662-54494-5_24. URL https://doi.org/10.1007/978-3-662-54494-5_24
  41. doi:10.1007/978-3-662-54494-5_23. URL https://doi.org/10.1007/978-3-662-54494-5_23
  42. doi:10.1007/s10009-019-00528-0. URL https://doi.org/10.1007/s10009-019-00528-0
  43. doi:10.1007/978-3-319-45994-3_2. URL https://doi.org/10.1007/978-3-319-45994-3_2
  44. doi:10.1007/978-3-642-22110-1_47. URL https://doi.org/10.1007/978-3-642-22110-1_47
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