Using simulated annealing for locating array construction

• Published in: Information and software technology Vol. 126; p. 106346
• Main Authors: Konishi, Tatsuya, Kojima, Hideharu, Nakagawa, Hiroyuki, Tsuchiya, Tatsuhiro
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-10-2020
• Subjects: Combinatorial interaction testing; Locating arrays; Simulated annealing; Software testing; Software testing; Locating arrays; Combinatorial interaction testing; Simulated annealing
• ISSN: 0950-5849; 1873-6025
• DOI: 10.1016/j.infsof.2020.106346

•The problem addressed is to construct locating arrays which can be used as test suites for locating interaction faults.•A simulated annealing based algorithm is proposed to construct locating arrays with a small number of rows (tests).•Some locating arrays obtained by the proposed algorithm are smaller than those known before.
Combinatorial interaction testing is known to be an efficient testing strategy for computing and information systems. Locating arrays are mathematical objects that are useful for this testing strategy, as they can be used as a test suite that permits fault localization as well as fault detection. In this application, each row of an array is used as an individual test.
This paper proposes an algorithm for constructing locating arrays with a small number of rows. Testing cost increases as the number of tests increases; thus the problem of finding locating arrays of small sizes is of practical importance.
The proposed algorithm uses simulated annealing, a meta-heuristic algorithm, to find locating array of a given size. The whole algorithm repeatedly executes the simulated annealing algorithm with the input array size being dynamically varied.
Experimental results show (1) that the proposed algorithm is able to construct locating arrays for problem instances of large sizes and (2) that, for problem instances for which nontrivial locating arrays are known, the algorithm is often able to generate locating arrays that are smaller than or at least equal to the known arrays.
Based on the results, we conclude that the proposed algorithm can produce small locating arrays and scale to practical problems.