Hybridization of Cuckoo-ACO algorithm for test case prioritization

• Published in: Journal of statistics & management systems Vol. 21; no. 4; pp. 539 - 546
• Main Authors: Panwar, Deepak, Tomar, Pradeep, Singh, Vijandra
• Format: Journal Article
• Language: English
• Published: New Delhi Taylor & Francis 04.07.2018; Taru Publications
• Subjects: 68T01; Algorithms; Ant colony optimization; Ant Colony Optimization (M-ACO); Cuckoo Search (CS); Dependence; Eggs; hybrid Cuckoo-ACO; Lethargy; Optimization techniques; Search algorithms
• ISSN: 0972-0510; 2169-0014
• DOI: 10.1080/09720510.2018.1466962

Regression testing is one of the types of maintenance testing and it is performed in case of bug-fixing or whenever there is any new functionality incorporate in the dynamic environment of software development process. Due to which the cost of development process increases because it directly affects the validation process. The addition of the new requirements with limited resources of development process in a time constrained environment definitely increase the cost. Efficient and effective test case selection from the available test suite becomes very critical problem in this scenario and this situation makes it important to applying of some techniques which are prioritization of test cases and selection of test cases for re-arrangement of test cases in a particular schedule and these test cases are selected in a particular sequence order to fulfillment of some selected criteria. This paper suggests Cuckoo Search (CS) algorithm followed by Modified Ant Colony Optimization (M-ACO) algorithm to conclude the test cases in an optimized order in time constrained environment. CS algorithm is inspired by some species of cuckoos having constrained brood lethargy and laying of their eggs in other host bird's nest. Due to dependency of this algorithm on one single parameter, cuckoo search unlike other optimization algorithms, is more efficient and very easy to implement. But the hybrid Cuckoo-ACO optimization technique is more appropriate for the test case selection and prioritization according to the proposed empirical study.