Constrained detecting arrays: Mathematical structures for fault identification in combinatorial interaction testing
Hao Jin, Ce Shi, Tatsuhiro Tsuchiya · Information and Software Technology · 2022
Detecting arrays are mathematical structures aimed at fault identification in combinatorial interaction testing. However, they cannot be directly applied to systems that have constraints on the test parameters. These constraints are prevalent in real-world systems. This paper proposes constrained detecting arrays (CDAs), an extension of detecting arrays, which can be used for systems with constraints. The properties and capabilities of CDAs are examined with rigorous arguments. Moreover, two algorithms are proposed for constructing CDAs: one is aimed at generating minimum CDAs, and the other is a heuristic algorithm aimed at fast generation of CDAs. The algorithms were experimentally evaluated using a benchmark dataset. Experimental results show that the first algorithm can generate minimum CDAs if a sufficiently long generation time is allowed, and the second algorithm can generate minimum or near-minimum CDAs in a reasonable time. CDAs extend the range of application of detecting arrays to systems with constraints. The two proposed algorithms have different advantages with respect to array size and generation time.