Adaptive Neighbor Heuristics Flower Pollination Algorithm Strategy for Sequence Test Generation

In recent years, a lot of metaheuristic algorithms have been successfully used for solving many software engineering problems. In the field of software testing, flower pollination algorithm (FPA) is used for finding minimum t-way tests where it is demonstrated its efficiency for generating sequence t-way tests; however, the existing results highlighting that FPA’s performance is still limited which can lead to poor results. This chapter proposes a new variant of FPA algorithm called adaptive neighbor heuristics flower pollination algorithm (NH-FPA) strategy for generating sequence t-way tests. NH-FPA aims to enhance the intensification capability by introducing neighboring heuristics search operators into original FPA. NH-FPA integrates neighboring heuristics operators such as insert, swap, and scramble, for better search space’s exploitation. Experiments are carried out to evaluate the proposed strategy by, first, comparing NH-FPA’s results with the existing t-way metaheuristics-based strategies. Then NH-FPA will compare with standard FPA to asses the contribution of introducing the neighboring heuristics operators into NH-FPA. Experimental results demonstrate that the proposed strategy outperforms most of the existing sequence t-way strategies in terms of generating minimum tests size and show a fast convergence rate compared with standard FPA.