Marginal Distribution Algorithm for Feature Model Test Configuration Generation

Generating test configuration for Software Product Line (SPL) is difficult, due to the exponential effect of feature combination. Pairwise testing can generate test input for a single software product that deviates from exhaustive testing, nevertheless proven to be effective. In the context of SPL testing, to generate minimal test configuration that maximizes pairwise coverage is not trivial, especially when dealing with a huge number of features and when constraints must be satisfied, which is the case in most SPL systems. In this paper, we propose an estimation of distribution algorithm, based on pairwise testing, to alleviate this problem. Comparisons are made against a greedy-based and a constraint handling based approach. The experiments demonstrate the feasibility of the proposed algorithm, such that it achieves better test configurations dissimilarity and at the same time maintain the test configuration size and pairwise coverage. This is supported by analysis using descriptive statistics.

@article{Sahid2023,
  title     = {Marginal Distribution Algorithm for Feature Model Test Configuration Generation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Mohd Zanes Sahid and Mohd Zainuri Saringat and Mohd Hamdi Irwan Hamzah and Nurezayana Zainal},
  doi       = {10.14569/IJACSA.2023.0140996},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140996}
}