GREEDY DISCRETE ANT COLONY OPTIMIZATION FOR HIGH COVERAGE TEST SUITE GENERATION

Abstract

Test suite optimization is significant problems in software engineering research to reduce testing cost of software program. Recently, few research works have been designed for test suite generation and reduction. However, there is a requirement for new technique to improve coverage rate of test suite generation and to remove redundant test cases. In order to overcome such limitations, a Greedy Discrete Ant Colony Optimization (GDACO) technique is proposed. The main objective of GDACO technique is to optimize the coverage capability of test suite generation. The GDACO technique initially used Ant Colony Optimization (ACO) algorithm for generating the test suites. The ACO algorithm selects test cases from test cases set based on trail’s probability and subsequently update pheromone trails until the maximum iteration is reached. This process results in generation of test suites for testing software programs. After that, GDACO technique used Greedy Discretization algorithm to test suite optimization. The Greedy Discretization algorithm designed in GDACO technique chooses the test cases which cover most test requirements and removes redundant test cases in test suites. Therefore, GDACO technique finally obtains minimal cardinality subset of test suites with higher coverage rate of faults. The GDACO technique conducts the experimental works on parameters such as coverage rate, average rate of test suite reduction and execution time. The experimental result demonstrates that the GDACO technique is able to improve the coverage rate of software faults and also increases the average rate of test suite reduction when compared to state-of-the-art-works.