International Journal of Electrical and Computer Engineering (IJECE) Vol. 9, No. 6, December 2019, pp. 4898~4903 ISSN: 2088-8708, DOI: 10.11591/ijece.v9i6.pp49898-4903  4898 Journal homepage: http://iaescore.com/journals/index.php/IJECE Hybrid swarm and GA based approach for software test case selection Palak, Preeti Gulia Department of Computer Science and Applications, Maharshi Dayanand University, India Article Info ABSTRACT Article history: Received Jan 22, 2019 Revised Apr 14, 2019 Accepted Jun 25, 2019 Being a crucial step and deciding factor for software reliability, software testing has evolved a long way and always attracted researchers due to various inherent challenges. The quality of a software application depends on the effectiveness of the testing carried out during development and maintenance phase. Testing is a crucial but time consuming activity that influences the overall cost of software development. Thus a minimal but efficient test suite selection is the need of the hour. This paper presents a hybrid technique based on swarm based search technique and GA (Genetic Algorithm) for selection of promising test cases to reduce the overall development cost and time of the application. We took component based software into consideration as they offer some inherent advantages over traditional software development paradigms. Keywords: Ant colony optimization Components Genetic algorithm Swarm intelligence Test case selection Copyright © 2019 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Palak, Department of Computer Science and Applications, Maharshi Dayanand University, Delhi By Pass, Rohtak, India. Email: palak.aug6@gmail.com 1. INTRODUCTION In this era of technology, the hardware and software industries are growing together at very fast pace to meet the growing need of smart devices. The smart gadgets have invaded our lives so badly that we can’t predict our future without them. The software embedded with these devices play a crucial role to provide best known user experiences to provide the intended functionality. This scenario raises many challenges in front of the software developers to fulfill the quality needs of the end user. Software testing is a crucial and unavoidable step to achieve the same. The role of test cases in the process of testing is very important to verify the functionality and detect faults. A software failure can claim many lives in case of critical systems. Moreover the development paradigms have evolved a long way from traditional procedural approach to a modular component based approach. Component based software engineering (CBSE) [1] evolved back in late 1980’s and growing since then. It works on the principle of reusability and the software in developed in small chunks called components. Each component has some set of functionality and interacts with other components through interfaces. They provide a black box view of the functionality. Commercial off the shelf (COTS) is gaining popularity with time. Considering the impracticality of the exhaustive testing, it becomes the need of the hour to select a promising suite of test data that is capable of providing higher fault coverage. Ant Colony Optimization [2] and Genetic Algorithm [3] are search based techniques that are inspired from nature and natural phenomenon. Swarm intelligence has provided us inspiration to solve many search based optimization problems. These are meta-heuristic techniques that are problem independent and can work with incomplete knowledge. In contrast to heuristics, meta- heuristics provide randomness during searching and prevent us to get stuck in local optima. ACO has been widely used in solving NP hard optimization problems in reasonable amount of time. It is inspired from the behavior of real ants in their natural habitat searching for food and traversing an intelligent path discovered through group behavior.