Optimal test sequence generation: an approach using ant colony optimisation Online publication date: Thu, 28-Aug-2014
by Praveen Ranjan Srivastava
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 1, No. 2, 2012
Abstract: As the demand for highly sophisticated software increase, the role of software testing becomes indispensible in the software development life cycle. Software testing is one of the most important factors for assessing the global competitive position of any software organisation. Thus, the automation of software testing is very essential. Software testing coverage criteria (i.e., generation of complete test sequences) are not very easily measured and quantified. Many attempts have been made to quantify the software testing coverage using various meta-heuristic models. The present work describes a method for increasing software testing efficiency by identifying the optimal test sequences for behaviour model. The aim of this paper is to present an algorithm, using ant colony optimisation (ACO), a swarm-based optimisation approach, to automate the process of optimised test sequence generation of software under test (SUT). A real-life example of verifying a proposed approach using ACO is given in this paper.
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