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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computational Systems Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
