Bridging the gap between ML solutions and their business requirements using feature interactions.

Machine Learning (ML) based solutions are becoming increasingly popular and pervasive. When testing such solutions, there is a tendency to focus on improving the ML metrics such as the F1-score and accuracy at the expense of ensuring business value and correctness by covering business requirements. In this work, we adapt test planning methods of classical software to ML solutions. We use combinatorial modeling methodology to define the space of business requirements and map it to the ML solution data, and use the notion of data slices to identify the weaker areas of the ML solution and strengthen them. We apply our approach to three real-world case studies and demonstrate its value.