An improved fuzzy adaptive teaching learning-based optimisation algorithm for generating pairwise test suites
by Fakhrud Din; Kamal Z. Zamli
International Journal of Computer Aided Engineering and Technology (IJCAET), Vol. 16, No. 2, 2022

Abstract: Pairwise testing has proved its applicability to adequately test software with a huge number of inputs. It can avoid the otherwise impractical exhaustive testing by employing an efficient sampling strategy. Strategies based on meta-heuristic algorithms offer optimal pairwise test suite sizes for software applications. A fuzzy adaptive teaching learning-based optimisation (ATLBO) algorithm has shown competitiveness against other meta-heuristic-based strategies in terms of pairwise test suite generation. Although useful, the present design of ATLBO is lacking in dealing with stagnation or abnormal convergence after some iterations. A remedial operator is introduced in ATLBO in order to address this issue and hence further enhance its convergence speed. With this modification, ATLBO is used for the pairwise test suite generation problem. From the conducted experiments, it can be concluded that the performance of ATLBO with remedial operator is comparable with other pairwise strategies based on hyper-heuristic, meta-heuristic and greedy algorithms.

Online publication date: Fri, 11-Feb-2022

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