Test Suite Optimization Using Firefly and Genetic Algorithm

Test Suite Optimization Using Firefly and Genetic Algorithm

Abhishek Pandey (University of Petroleum and Energy Studies, Dehradun & Birla Institute of Technology, Mesra-Ranchi, India) and Soumya Banerjee (Conservatoire Nationale des Arts et Metiers & INRIA Paris ex-visiting CNRS INSA de Lyon, Paris, France)
DOI: 10.4018/IJSSCI.2019010103

Abstract

Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.
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Agrawal & Kaur (2018) compares the performance of two metaheuristics namely ant colony and hybrid particle swarm optimization exclusively for test cases selection problem. The quality parameters in this research are execution time and fault coverage. Experiments were performed using Matlab. This article demonstrates the significance of hybrid algorithms for test cases selection problem in software engineering.

De Oliveira Neto et al. (2018) evaluates similarity-based test case selection on integration level tests. The results confirm the existing strong evidence that similarity-based test case selection is the major candidate for test optimization.

Choudhary, Agrawal & Kaur (2018) presents an effective method for test case selection using Pareto based multi-objective harmony research. Fors et al. (2019) present a safe regression test case selection for Modelica using static analysis.

Nogueira et al. (2019) discuss model-based testing using natural language description of use cases. It is important to note that formal description of use cases using mathematical notation poses challenges in test case generation and selection process. To overcome this issues, use cases are described in natural language that is easily understandable to the testing team.

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