WCA: A Weighting Local Search for Constrained Combinatorial Test Optimization

Abstract Context: Covering array generation (CAG) is the core task of Combinatorial interaction testing (CIT), which is widely used to discover interaction faults in real-world systems. Considering the universality, constrained covering array generation (CCAG) is more in line with the characteristics of applications, and has attracted a lot of researches in the past few years. Objective: In CIT, a covering array (CA) with smaller size means lower cost of testing, particularly for the systems where the execution of a test suite is time consuming. As constraints between parameters are ubiquitous in real systems, this work is dedicated to more efficient algorithms for CCAG. Specifically, we aim to develop a heuristic search algorithm for CCAG, which allows generating CAs with smaller size in a limited time when compared with existing algorithms. Method: We propose a weighting local search algorithm named WCA, which makes use of weights associated with the tuples and dynamically adjusts them during the search, helping the algorithm to avoid search stagnation. As far as we know, this is the first weighting local search for solving CCAG. Results: We apply WCA to a wide range of benchmarks, including real-world ones and synthetic ones. The results show that WCA achieves a significant improvement over three state-of-the-art competitors in 2-way and 3-way CCAG, in terms of both effectiveness and efficiency. The importance of weighting is also reflected by the experimental comparison between WCA and its alternative algorithm without the weighing mechanism. Conclusion: WCA is an effective heuristic algorithm for CCAG to obtain smaller CAs efficiently, and the weighting mechanism plays a crucial role.