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Incomplete MaxSAT approaches for combinatorial testing

Author

Listed:
  • Carlos Ansótegui

    (University of Lleida)

  • Felip Manyà

    (Artificial Intelligence Research Institute (IIIA, CSIC))

  • Jesus Ojeda

    (University of Lleida)

  • Josep M. Salvia

    (University of Lleida)

  • Eduard Torres

    (University of Lleida)

Abstract

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.

Suggested Citation

  • Carlos Ansótegui & Felip Manyà & Jesus Ojeda & Josep M. Salvia & Eduard Torres, 2022. "Incomplete MaxSAT approaches for combinatorial testing," Journal of Heuristics, Springer, vol. 28(4), pages 377-431, August.
    • Handle: RePEc:spr:joheur:v:28:y:2022:i:4:d:10.1007_s10732-022-09495-3
    DOI: 10.1007/s10732-022-09495-3
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    References listed on IDEAS

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    1. Carlos Ansótegui & Joel Gabàs & Jordi Levy, 2016. "Exploiting subproblem optimization in SAT-based MaxSAT algorithms," Journal of Heuristics, Springer, vol. 22(1), pages 1-53, February.
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