Hybrid Artificial Bee Colony (HABC) Strategy is latterly developed based on hybridize of an artificial bee colony (ABC) algorithm with a particle swarm optimization (PSO) algorithm. In order to ensure that HABC could perform for t-way testing as useful as other strategies to generate best performance, there are a number of parameters of an HABC algorithm such as the number of colony size (NBees), the number of food source, limit, the number of cycles (maxCycle), weight factor (w) and learning factors (C1, C2) that required to be tuned. In this paper, the process of parameters tuning for hybrid artificial bee colony algorithm has been shown as well as t-way testing, where has been adopted a standard covering array CA (N, 2, 57). The obtained experiment results illustrate that HABC strategy can generate the most minimum and sufficiently results compared to other strategies. © 2019, Blue Eyes Intelligence Engineering and Sciences Publication. All rights reserved.