IPO-MAXSAT: The In-Parameter-Order Strategy combined with MaxSAT solving for Covering Array Generation

• Published in: Proceedings (International Symposium on Symbolic and Numeric Algorithms for Scientific Computing) pp. 71 - 79
• Main Authors: Hiess, Irene, Kampel, Ludwig, Wagner, Michael, Simos, Dimitris E.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2022
• Subjects: Algorithms; Combinatorial testing; Covering arrays; Optimization; SAT solving; Scientific computing; Software algorithms
• ISSN: 2470-881X
• DOI: 10.1109/SYNASC57785.2022.00021

Covering arrays (CAs) are combinatorial designs that represent the backbone of combinatorial testing, which is applied most prominently in automated software testing. The generation of optimized CAs is a difficult combinatorial optimization problem being subject to ongoing research. Previous studies have shown that many different algorithmic approaches are best suited for different instances of CAs. In this paper we present the IPO-MAXSAT algorithm, which adopts the prominent inparameter-order (IPO) strategy for CA generation and uses MaxSAT solving to optimize the occurring sub-problems. We devise three different algorithmic variants that use a MaxSAT solver for different sub-problems. These variants are evaluated in an extensive set of experiments where we also consider the usage of different MaxSAT solvers. Further, we provide a comparison against various other algorithms realizing the IPO strategy as well as the state of the art.