Regression test optimization and prioritization using Honey Bee optimization algorithm with fuzzy rule base

Abstract

Regression testing is a maintenance level activity performed on a modified program to instill confidence in the software’s reliability. Prioritization of test case arranges the regression test suite to detect the faults earlier in the testing process. The test cases necessary for validating the recent changes and finding the maximum faults in minimum time are selected. In this manuscript, an optimization algorithm (Bee Algorithm) based on the intelligent foraging behavior of honey bee swarm has been proposed that can enhance the rate of fault detection in test case prioritization. The bee algorithm, along with the fuzzy rule base, reduces the test cases’ volume by selecting the test cases from the pre-existing test suite. The proposed algorithm developed for enhancing the fault detection rate in minimum time is inspired by the behavior of two types of worker bees, namely scout bees and forager bees. These worker bees are responsible for the maintenance, progress, and growth of the colony. The proposed approach is implemented on two projects. The prioritization result is quantified by using the average percentage of fault detection (APFD) metric. Compared with other existing prioritization techniques like no prioritization, reverse prioritization, random prioritization, and previous work, the proposed algorithm outperforms all in fault detection rate. The effectiveness of the proposed algorithm is represented by using the APFD graphs and charts.