Bio-Inspired Computational Intelligence and Its Application to Software Testing

Bio-Inspired Computational Intelligence and Its Application to Software Testing

Abhishek Pandey (UPES Dehradun, India) and Soumya Banerjee (BIT Mesra, India)
DOI: 10.4018/978-1-5225-2128-0.ch014

Abstract

Bio inspired algorithms are computational procedure inspired by the evolutionary process of nature and swarm intelligence to solve complex engineering problems. In the recent times it has gained much popularity in terms of applications to diverse engineering disciplines. Now a days bio inspired algorithms are also applied to optimize the software testing process. In this chapter authors will discuss some of the popular bio inspired algorithms and also gives the framework of application of these algorithms for software testing problems such as test case generation, test case selection, test case prioritization, test case minimization. Bio inspired computational algorithms includes genetic algorithm (GA), genetic programming (GP), evolutionary strategies (ES), evolutionary programming (EP) and differential evolution(DE) in the evolutionary algorithms category and Ant colony optimization(ACO), Particle swarm optimization(PSO), Artificial Bee Colony(ABC), Firefly algorithm(FA), Cuckoo search(CS), Bat algorithm(BA) etc. in the Swarm Intelligence category(SI).
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2. Backgrounds

2.1. Computational Methods Inspired by Biological Processes of Nature

Inspiration from nature and biological processes have motivated to the development of various computational algorithms in order to solve complex problems. These algorithms are classified as evolutionary and swarm intelligence based algorithms. Evolutionary algorithms are based on the principle of survival of the fittest or natural selection (J.H Holland, 1975). Swarm intelligence is based on the cooperative group intelligence of swarms or collective behavior of insect colonies and other animal colonies (E.Bonabeau, M. Dorigo, & G.Theraulaz, 1999).

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