Incremental Identification of T-Wise Feature Interactions.

Developers of configurable software use the concept of selecting and deselecting features to create different variants of a software product. In this context, one of the most challenging aspects is to identify unwanted interactions between those features. Due to the combinatorial explosion of the number of potentially interacting features, it is currently an open question how to systematically identify a particular feature interaction that causes a specific fault in a set of software products. In this paper, we propose an incremental approach to identify such t-wise feature interactions based on testing additional configurations in a black-box setting. We present the algorithm Inciident, which generates and selects new configurations based on a divide-and-conquer strategy to efficiently identify the feature interaction with a preferably minimal number of configurations. We evaluate our approach by considering simulated and real interactions of different sizes for 48 real-world feature models. Our results show that on average, Inciident requires 80 % less configurations to identify an interaction than using randomly selected configurations.