Incomplete MaxSAT Approaches for Combinatorial Testing
This work addresses the detection of system failures in combinatorial testing, but it is incremental as it builds on existing MaxSAT technology with new encodings and algorithms.
The authors tackled the Covering Array Number problem in combinatorial testing by developing SAT and MaxSAT encodings to compute optimal and suboptimal solutions, with experimental results showing good performance compared to state-of-the-art tools.
We present a Satisfiability (SAT)-based approach for building Mixed Covering Arrays with Constraints of minimum length, referred to as the Covering Array Number problem. This problem is central in Combinatorial Testing for the detection of system failures. In particular, we show how to apply Maximum Satisfiability (MaxSAT) technology by describing efficient encodings for different classes of complete and incomplete MaxSAT solvers to compute optimal and suboptimal solutions, respectively. Similarly, we show how to solve through MaxSAT technology a closely related problem, the Tuple Number problem, which we extend to incorporate constraints. For this problem, we additionally provide a new MaxSAT-based incomplete algorithm. The extensive experimental evaluation we carry out on the available Mixed Covering Arrays with Constraints benchmarks and the comparison with state-of-the-art tools confirm the good performance of our approaches.