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Machine Learning for Feature Constraints Discovery

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Handbook of Re-Engineering Software Intensive Systems into Software Product Lines

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Abstract

Constraints among features are central to the success and quality of software product lines (SPLs). Unfortunately, the number of potential interactions and dependencies, materialized as logical constraints, grows as the number of features increases in an SPL. In particular, it is easy to forget a constraint and thus mistakenly authorizes invalid products. Developers thus struggle to identify and track constraints throughout the engineering of more and more complex SPLs.

In this chapter, we show how to leverage statistical machine learning (and more specifically decision trees) to automatically prevent the derivation of invalid products through the synthesis of constraints. The key principle is to try and test some product of an SPL and then identify what individual features or combinations of features (if any) are causing their non-validity (e.g., a product does not compile). A sample of derived products is used to train a classifier (here a decision tree but other classifiers might also be used as long as constraints can be easily extracted) that can classify any remaining products of the SPL. We illustrate the chapter through different application domains and software systems (a video generator, parametric programs for 3D printing, or the Linux kernel). We also discuss the cost, benefits, and applicability of the method.

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Notes

  1. 1.

    The difference between a class and a label lies in the fact that labels are determined by the annotator or an “oracle” while classes are predicted by a classifier.

  2. 2.

    We call a feature selected if its value is set to 1; if it is set to 0, the feature is deselected.

References

  1. Acher, M., Martin, H., Alves Pereira, J., Blouin, A., Eddine Khelladi, D., Jézéquel, J.M.: Learning From Thousands of Build Failures of Linux Kernel Configurations. Technical report, Inria ; IRISA (2019). URL https://hal.inria.fr/hal-02147012

  2. Acher, M., Temple, P., Jezequel, J.M., Galindo, J.A., Martinez, J., Ziadi, T.: Varylatex: Learning paper variants that meet constraints. In: Proceedings of the 12th International Workshop on Variability Modelling of Software-Intensive Systems, pp. 83–88. ACM (2018)

    Google Scholar 

  3. Al-Msie’Deen, R.A., Huchard, M., Seriai, A.D., Urtado, C., Vauttier, S.: Concept lattices: a representation space to structure software variability. In: ICICS: International Conference on Information and Communication Systems. Irbid, Jordan (2014)

    Google Scholar 

  4. Amand, B., Cordy, M., Heymans, P., Acher, M., Temple, P., Jézéquel, J.M.: Towards learning-aided configuration in 3d printing: Feasibility study and application to defect prediction. In: Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems, p. 7. ACM (2019)

    Google Scholar 

  5. Barr, E.T., Harman, M., McMinn, P., Shahbaz, M., Yoo, S.: The oracle problem in software testing: A survey. IEEE transactions on software engineering 41(5), 507–525 (2014)

    Article  Google Scholar 

  6. Bécan, G., Acher, M., Baudry, B., Nasr, S.B.: Breathing ontological knowledge into feature model synthesis: an empirical study. Empir. Softw. Eng. 21(4), 1794–1841 (2016)

    Article  Google Scholar 

  7. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth and Brooks, Monterey, CA (1984)

    Google Scholar 

  8. Czarnecki, K., Wasowski, A.: Feature diagrams and logics: There and back again. In: SPLC’07 (2007)

    Google Scholar 

  9. Dietrich, C., Tartler, R., Schröder-Preikschat, W., Lohmann, D.: A robust approach for variability extraction from the linux build system. In: Proceedings of the 16th International Software Product Line Conference-Volume 1, pp. 21–30 (2012)

    Google Scholar 

  10. Gargantini, A., Petke, J., Radavelli, M.: Combinatorial interaction testing for automated constraint repair. In: 2017 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), pp. 239–248. IEEE (2017)

    Google Scholar 

  11. Guo, J., Czarnecki, K., Apel, S., Siegmund, N., Wasowski, A.: Variability-aware performance prediction: A statistical learning approach. In: ASE (2013)

    Google Scholar 

  12. Haslinger, E.N., Lopez-Herrejon, R.E., Egyed, A.: On extracting feature models from sets of valid feature combinations. In: FASE (2013)

    Google Scholar 

  13. James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning: with Applications in R. Springer (2013). URL https://faculty.marshall.usc.edu/gareth-james/ISL/

  14. Kaltenecker, C., Grebhahn, A., Siegmund, N., Guo, J., Apel, S.: Distance-based sampling of software configuration spaces. In: Proceedings of the IEEE/ACM International Conference on Software Engineering (ICSE). ACM (2019)

    Google Scholar 

  15. Kittur, A., Chi, E.H., Suh, B.: Crowdsourcing user studies with mechanical turk. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp. 453–456 (2008)

    Google Scholar 

  16. Krismayer, T., Rabiser, R., Grünbacher, P.: Mining constraints for event-based monitoring in systems of systems. In: ASE, pp. 826–831. IEEE Press (2017)

    Google Scholar 

  17. Lillack, M., Müller, J., Eisenecker, U.W.: Improved prediction of non-functional properties in software product lines with domain context. Software Engineering 2013 (2013)

    Google Scholar 

  18. Lora-Michiels, A., Salinesi, C., Mazo, R.: A Method based on Association Rules to Construct Product Line Model. In: 4th International Workshop on Variability Modelling of Software-intensive Systems (VaMos), p. 50. Linz, Austria (2010). URL https://hal.archives-ouvertes.fr/hal-00707527

  19. Martin, H., Pereira, J.A., Acher, M., Jézéquel, J.: A comparison of performance specialization learning for configurable systems. In: SPLC ’21: 25th ACM International Systems and Software Product Line Conference. ACM (2021)

    Google Scholar 

  20. Martinez, J., Ziadi, T., Bissyandé, T.F., Klein, J., Le Traon, Y.: Bottom-up adoption of software product lines: a generic and extensible approach. In: Proceedings of the 19th International Conference on Software Product Line, pp. 101–110 (2015)

    Google Scholar 

  21. Martinez, J., Ziadi, T., Mazo, R., Bissyandé, T.F., Klein, J., Le Traon, Y.: Feature Relations Graphs: A Visualisation Paradigm for Feature Constraints in Software Product Lines. In: IEEE Working Conference on Software Visualization (VISSOFT 2014), pp. 50–59. Victoria, Canada (2014)

    Google Scholar 

  22. Nadi, S., Berger, T., Kästner, C., Czarnecki, K.: Mining configuration constraints: Static analyses and empirical results. In: ICSE (2014)

    Google Scholar 

  23. Pereira, J.A., Acher, M., Martin, H., Jézéquel, J.: Sampling effect on performance prediction of configurable systems: A case study. In: J.N. Amaral, A. Koziolek, C. Trubiani, A. Iosup (eds.) ICPE ’20: ACM/SPEC International Conference on Performance Engineering, Edmonton, AB, Canada, April 20-24, 2020, pp. 277–288. ACM (2020)

    Google Scholar 

  24. Pereira, J.A., Martin, H., Acher, M., Jézéquel, J.M., Botterweck, G., Ventresque, A.: Learning software configuration spaces: A systematic literature review (2019)

    Google Scholar 

  25. Pett, T., Thüm, T., Runge, T., Krieter, S., Lochau, M., Schaefer, I.: Product sampling for product lines: The scalability challenge. In: Proceedings of the 23rd International Systems and Software Product Line Conference-Volume A, pp. 78–83 (2019)

    Google Scholar 

  26. Plazar, Q., Acher, M., Perrouin, G., Devroey, X., Cordy, M.: Uniform sampling of SAT solutions for configurable systems: Are we there yet? In: ICST 2019 - 12th International Conference on Software Testing, Verification, and Validation, pp. 1–12. Xian, China (2019). URL https://hal.inria.fr/hal-01991857

  27. Pohl, K., Böckle, G., van der Linden, F.J.: Software product line engineering: foundations, principles and techniques. Springer, Berlin Heidelberg (2005)

    Book  MATH  Google Scholar 

  28. Ryssel, U., Ploennigs, J., Kabitzsch, K.: Extraction of feature models from formal contexts. In: I. Schaefer, I. John, K. Schmid (eds.) Software Product Lines - 15th International Conference, SPLC 2011, Munich, Germany, August 22-26, 2011. Workshop Proceedings (Volume 2), p. 4. ACM (2011)

    Google Scholar 

  29. Safdar, S.A., Lu, H., Yue, T., Ali, S.: Mining cross product line rules with multi-objective search and machine learning. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1319–1326. ACM (2017)

    Google Scholar 

  30. Sarkar, A., Guo, J., Siegmund, N., Apel, S., Czarnecki, K.: Cost-efficient sampling for performance prediction of configurable systems (t). In: ASE, pp. 342–352. IEEE (2015)

    Google Scholar 

  31. Shatnawi, A., Seriai, A., Sahraoui, H.: Recovering architectural variability of a family of product variants. In: International Conference on Software Reuse, pp. 17–33. Springer (2015)

    Google Scholar 

  32. Siegmund, N., Grebhahn, A., Apel, S., Kästner, C.: Performance-influence models for highly configurable systems. In: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, pp. 284–294 (2015)

    Google Scholar 

  33. Siegmund, N., Kolesnikov, S.S., Kästner, C., Apel, S., Batory, D.S., Rosenmüller, M., Saake, G.: Predicting performance via automated feature-interaction detection. In: ICSE, pp. 167–177 (2012)

    Google Scholar 

  34. Siegmund, N., Rosenmüller, M., Kästner, C., Giarrusso, P.G., Apel, S., Kolesnikov, S.S.: Scalable prediction of non-functional properties in software product lines. In: 15th International Software Product Line Conference (SPLC), pp. 160–169 (2011)

    Google Scholar 

  35. Siegmund, N., Rosenmüller, M., Kuhlemann, M., Kästner, C., Apel, S., Saake, G.: SPL Conqueror: Toward optimization of non-functional properties in software product lines. Software Quality Journal 20(3), 487–517 (2012)

    Article  Google Scholar 

  36. Siegmund, N., Sobernig, S., Apel, S.: Attributed variability models: outside the comfort zone. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp. 268–278. ACM (2017)

    Google Scholar 

  37. Strüber, D., Mukelabai, M., Krüger, J., Fischer, S., Linsbauer, L., Martinez, J., Berger, T.: Facing the truth: Benchmarking the techniques for the evolution of variant-rich systems. In: Proceedings of the 23rd International Systems and Software Product Line Conference-Volume A, pp. 177–188 (2019)

    Google Scholar 

  38. Temple, P., Acher, M., Jézéquel, J., Barais, O.: Learning contextual-variability models. IEEE Software 34(6), 64–70 (2017)

    Article  Google Scholar 

  39. Temple, P., Galindo Duarte, J.A., Acher, M., Jézéquel, J.M.: Using Machine Learning to Infer Constraints for Product Lines. In: Software Product Line Conference (SPLC). Beijing, China (2016)

    Google Scholar 

  40. Temple, P., Perrouin, G., Acher, M., Biggio, B., Jézéquel, J.M., Roli, F.: Empirical assessment of generating adversarial configurations for software product lines. Empirical Software Engineering 26(1), 1–49 (2021)

    Article  Google Scholar 

  41. Thüm, T.: A bdd for linux? the knowledge compilation challenge for variability. In: Proceedings of the 24th ACM Conference on Systems and Software Product Line: Volume A-Volume A, pp. 1–6 (2020)

    Google Scholar 

  42. Turk, A.M.: Amazon mechanical turk. Retrieved August 17, 2012 (2012)

    Google Scholar 

  43. Westermann, D., Happe, J., Krebs, R., Farahbod, R.: Automated inference of goal-oriented performance prediction functions. In: ASE, pp. 190–199. ACM (2012)

    Google Scholar 

  44. Yilmaz, C., Cohen, M.B., Porter, A.A.: Covering arrays for efficient fault characterization in complex configuration spaces. IEEE Transactions on Software Engineering 32(1), 20–34 (2006)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Rennes, IRISA, CNRS, Rennes, France

    Hugo Martin, Mathieu Acher & Jean-Marc Jézéquel

  2. University of Namur, Namur, Belgium

    Paul Temple

  3. Pontifical Catholic University of Rio de Janeiro, PUC-Rio, Rio de Janeiro, Brazil

    Juliana Alves Pereira

Authors
  1. Hugo Martin
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  2. Paul Temple
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  3. Mathieu Acher
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  4. Juliana Alves Pereira
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  5. Jean-Marc Jézéquel
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Corresponding author

Correspondence to Mathieu Acher .

Editor information

Editors and Affiliations

  1. École de Technologie Supérieure, Universite du Québec, Montreal, QC, Canada

    Roberto E. Lopez-Herrejon

  2. Tecnalia, Basque Research and Technology Alliance (BRTA), Derio, Spain

    Jabier Martinez

  3. Pontifical Catholic University of Rio de Janeiro, Johannes Kepler University of Linz, Linz, Austria

    Wesley Klewerton Guez Assunção

  4. Sorbonne Universités & CNRS-LIP6, Paris, Paris, France

    Tewfik Ziadi

  5. IRISA-Inria, Institut Universitaire de France (IUF) & University of Rennes 1, Rennes, France

    Mathieu Acher

  6. Departamento de Informática, Federal University of Parana (UFPR), Curitiba, Brazil

    Silvia Vergilio

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Martin, H., Temple, P., Acher, M., Pereira, J.A., Jézéquel, JM. (2023). Machine Learning for Feature Constraints Discovery. In: Lopez-Herrejon, R.E., Martinez, J., Guez Assunção, W.K., Ziadi, T., Acher, M., Vergilio, S. (eds) Handbook of Re-Engineering Software Intensive Systems into Software Product Lines. Springer, Cham. https://doi.org/10.1007/978-3-031-11686-5_7

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  • DOI: https://doi.org/10.1007/978-3-031-11686-5_7

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