Emerging Trends in Engineering and Management
Systematic Literature Review in Software Test Data Generation
Authors: Gagan Kumar, Vinay Chopra and Dinesh Gupta
Publishing Date: 09-02-2023
ISBN: 978-81-955020-3-5
Abstract
In software development, software testing is an important practice which comprises of different activities. It is the time consuming and cost oriented process. In testing, it is very important to select test data generation process wisely because testing efficiency is highly dependent on the data used and it may affect the cost and time. Soft computing algorithms explore test data in search-based software testing to optimize the coverage metric, which can be called an optimization challenge. Some Meta-Heuristics algorithms (Artificial Bee Colony, Particle Swarm Optimization, Genetic Algorithm, Firefly Algorithm and Ant Colony Optimization Algorithms) are selected in this paper for comparative study along with Artificial Immune Algorithms (Negative Selection Algorithm, Clonal selection, and Hybrid Negative Selection Algorithm). The Immune algorithm also have significant impact in engineering applications and in the field of software test data generation. A survey on automated test data generation has been done on the various criteria such as type of objective function use, type and number of experiments performed for specific technique, comparison with other techniques, types of parameters used and the performance of the algorithm. From this survey it has been observed that the immune algorithms outperform meta-heuristic algorithms in terms of average coverage, average generation, cost, and average test data generated. But somehow the number of comparisons to generate test data in immune algorithms is more than the Metaheuristic algorithms.
Keywords
Test data generation, ACO, NSA, PSO, GA, ABC, FA.
Cite as
Gagan Kumar, Vinay Chopra and Dinesh Gupta, "Systematic Literature Review in Software Test Data Generation", In: Vikram Dhiman and Pooja Dhand (eds), Emerging Trends in Engineering and Management, SCRS, India, 2023, pp. 91-107. https://doi.org/10.56155/978-81-955020-3-5-11