Adapting ant colony optimization to generate test data for software structural testing

作     者：Mao, Chengying Xiao, Lichuan Yu, Xinxin Chen, Jinfu 

作者机构：Jiangxi Univ Finance & Econ Sch Software & Commun Engn Nanchang 330013 Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China Jiangsu Univ Sch Comp Sci & Telecommun Engn Zhenjiang 212013 Jiangsu Peoples R China 

出 版 物：《SWARM AND EVOLUTIONARY COMPUTATION》 (群与进化计算)

年 卷 期：2015年第20卷

页      面：23-36页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China (NSFC) [61462030, 61202110, 61262010] Science Foundation of Jiangxi Educational Committee [GJJ14332] Open Foundation of State Key Laboratory of Software Engineering [SKLSE2010-08-23] Program for Outstanding Young Academic Talent in Jiangxi University of Finance and Economics 

主　　题：Test data generation Meta-heuristic search Ant colony optimization Branch coverage Fitness function Experimental evaluation 

摘      要：In general, software testing has been viewed as an effective way to improve software quality and reliability. However, the quality of test data has a significant impact on the fault-revealing ability of software testing activity. Recently, search-based test data generation has been treated as an operational approach to settle this difficulty. In the paper, the basic ACO algorithm is reformed into discrete version so as to generate test data for structural testing. First, the technical roadmap of combining the adapted ACO algorithm and test process together is introduced. In order to improve algorithm s searching ability and generate more diverse test inputs, some strategies such as local transfer, global transfer and pheromone update are defined and applied. The coverage for program elements is a special optimization objective, so the customized fitness function is constructed in our approach through comprehensively considering the nesting level and predicate type of branch. To validate the effectiveness of our ACO-based test data generation method, eight well-known programs are utilized to perform the comparative analysis. The experimental results show that our approach outperforms the existing simulated annealing and genetic algorithm in the quality of test data and stability, and is comparable to particle swarm optimization-based method. In addition, the sensitivity analysis on algorithm parameters is also employed to recommend the reasonable parameter settings for practical applications. (C) 2014 Elsevier B.V. All rights reserved.