MLCHECK–Property-Driven Testing of Machine Learning Classifiers - Sharma, Arnab; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille; Wehrheim, Heike; SFB 901; SFB 901 - Project Area B; SFB 901 - Subproject B3; SFB 901 - Subproject B2 (Proceedings of the 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021)
In recent years, we observe an increasing amount of software with machine learning components being deployed. This poses the question of quality assurance for such components: how can we validate whether specified requirements are fulfilled by a machine learned software? Current testing and verification approaches either focus on a single requirement (e.g., fairness) or specialize on a single type of machine learning model (e.g., neural networks). In this paper, we propose property-driven testing of machine learning models. Our approach MLCheck encompasses (1) a language for property specification, and (2) a technique for systematic test case generation. The specification language is comparable to property-based testing languages. Test case generation employs advanced verification technology for a systematic, property dependent construction of test suites, without additional user supplied generator functions. We evaluate MLCheck using requirements and data sets from three different application areas (software discrimination, learning on knowledge graphs and security). Our evaluation shows that despite its generality MLCheck can even outperform specialised testing approaches while having a comparable runtime
accepted
Keywords: conference paper