Metaheuristic Techniques for Test Case Generation: A Review

Metaheuristic Techniques for Test Case Generation: A Review

Rashmi Rekha Sahoo (ITER College, Siksha 'O' Anusandhan University, Bhubaneswar, India) and Mitrabinda Ray (ITER College, Siksha 'O' Anusandhan University, Bhubaneswar, India)
Copyright: © 2018 |Pages: 14
DOI: 10.4018/JITR.2018010110
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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.
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Meta Heuristic Search Based Techniques

A metaheuristic is a higher-level procedure to find a heuristic to provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity (Harman et al., 2007). According to search strategies, it can be classified as local search and global search methods. Global algorithms make balance between exploration and exploitation of the search space by providing globally best solution. These algorithms terminate when a user defined stopping criteria is met. Unlike global search algorithm, local search algorithms do not give emphasis to exploration. They try to find a good solution among the neighbouring solutions. These algorithms terminate when they do not get any better neighbour solution and hence becomes locally optimal (Leonora et al., 2009) (see Figure 1).

Figure 1.

Search based methods


In this section, some most widely used local search algorithms like Hill Climbing (HC), Tabu Search (TS) and Simulated Annealing (SA) and global search algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Cuckoo Search (CS) are discussed. Also, their advantages and disadvantages and their application in suitable problems are mainly focused.

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