Open AccessA Hybrid Approach for Test Case Prioritization using PSO Based on Software Quality Metrics
Author(s) -
K. S. Kavi Kumar,
A. Muthukumaravel
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.12.16046
Subject(s) - computer science , particle swarm optimization , cuckoo search , software quality , test case , reliability engineering , software , code coverage , quality assurance , quality (philosophy) , software quality assurance , regression testing , prioritization , data mining , machine learning , software system , engineering , software development , software construction , philosophy , operations management , regression analysis , external quality assessment , epistemology , management science , programming language
Effective functionality checking of any software application is the crucial event that determines the quality of outcome obtained. Generally, checking scenarios that involves multiple test cases in mixture with multiple components is time consuming and also increases the quality assurance cost. Selection of suitable method/approach for optimization and prioritization of test cases as well as appropriate evaluation of the application would result in reduction of fault detection effort without appreciable information loss and further would also significantly decrease the clearing up cost. In the proposed method, test cases are optimized and then prioritized by Particle Swarm Optimization algorithm (PSO) and Improved Cuckoo Search algorithm (ICSA), respectively. Finally, the result will be evaluated for software quality measures.