Abstract
Code quality is a requirement for successful and sustainable software development. The emergence of Artificial Intelligence and data driven Machine Learning in current applications makes customized solutions for both data as well as code quality a requirement. The diversity and the stochastic nature of Machine Learning algorithms require different test methods, each of which is suitable for a particular method. Conventional unit tests in test-automation environments provide the common, well-studied approach to tackle code quality issues, but Machine Learning applications pose new challenges and have different requirements, mostly as far the numerical computations are concerned. In this research work, a concrete use of property-based testing for quality assurance in the parameter learning algorithm of a probabilistic graphical model is described. The necessity and effectiveness of this method in comparison to unit tests is analyzed with concrete code examples for enhanced retraceability and interpretability, thus highly relevant for what is called explainable AI.
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Notes
- 1.
https://schule.learninglab.tugraz.at/einmaleins/, Last accessed 26 April 2020.
- 2.
https://docs.pytest.org/, Last accessed 10 March 2020.
- 3.
https://hypothesis.readthedocs.io/en/latest/, Last accessed 10 March 2020.
References
On testing machine learning programs. J. Syst. Softw. 164, 110542 (2020). https://doi.org/10.1016/j.jss.2020.110542
Bishop, C.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Braiek, H.B., Khomh, F.: On testing machine learning programs. J. Syst. Softw. 164, 110542 (2020)
Dutta, S., Legunsen, O., Huang, Z., Misailovic, S.: Testing probabilistic programming systems. In: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 574–586 (2018)
Grosse, R.B., Duvenaud, D.K.: Testing MCMC code. arXiv preprint. arXiv:1412.5218 (2014)
Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs, 2nd edn. Springer, New York (2007)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)
Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT press, Cambridge (2012)
Nilsson, R.: ScalaCheck: the definitive guide. Artima (2014)
Okken, B.: Python Testing with Pytest: Simple, Rapid, Effective, and Scalable. Pragmatic Bookshelf (2017)
Pfeffer, A.: Practical Probabilistic Programming. Manning Publications, Greenwich (2016)
Saranti, A., Taraghi, B., Ebner, M., Holzinger, A.: Insights into learning competence through probabilistic graphical models. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2019. LNCS, vol. 11713, pp. 250–271. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29726-8_16
Sharma, A., Wehrheim, H.: Testing monotonicity of machine learning models. arXiv:2002.12278 (2020)
Taraghi, B., Saranti, A., Legenstein, R., Ebner, M.: Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 449–453 (2016)
Zhang, J.M., Harman, M., Ma, L., Liu, Y.: Machine learning testing: survey, landscapes and horizons. arXiv preprint arXiv:1906.10742 (2019)
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Saranti, A., Taraghi, B., Ebner, M., Holzinger, A. (2020). Property-Based Testing for Parameter Learning of Probabilistic Graphical Models. In: Holzinger, A., Kieseberg, P., Tjoa, A., Weippl, E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2020. Lecture Notes in Computer Science(), vol 12279. Springer, Cham. https://doi.org/10.1007/978-3-030-57321-8_28
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