Metaheuristic Techniques for Test Case Generation

Abstract

The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.

Human Activity Recognition Data Analysis: History, Evolutions, and New Trends

“…The constructive metaheuristics [94] are oriented to the procedures that try to obtain a solution from the analysis and gradual selection of the components that form it. Search metaheuristics [95] guide procedures that use transformations or moves to traverse the space of alternative solutions and exploit the associated environment structures. Evolutionary metaheuristics [96] are focused on procedures based on solution sets that evolve over the solution space.…”

Hybrid black widow optimization with iterated greedy algorithm for gene selection problems

“…This type of algorithm can be employed as an optimizer for the learning model [ 20 , 21 ]. Metaheuristic algorithms have captured the interest of several researchers in recent years because these algorithms have the capacity to solve diverse problems in different fields [ 22 , 23 ]. Consequently, several recommendations have been made that metaheuristic algorithms in biology, math, or physics are influenced by almost distinct phenomena [ 24 , 25 ].…”