Open AccessRegression Test Suite Reduction using an Hybrid Technique Based on BCO And Genetic Algorithm
Author(s) -
Bharti Suri,
Isha Mangal,
Varun Srivastava
Publication year - 2013
Publication title -
international journal of computer science and informatics
Language(s) - English
Resource type - Journals
ISSN - 2231-5292
DOI - 10.47893/ijcsi.2013.1113
Subject(s) - crossover , computer science , regression testing , genetic algorithm , test suite , reduction (mathematics) , inheritance (genetic algorithm) , algorithm , test case , selection (genetic algorithm) , set (abstract data type) , mutation , mathematical optimization , artificial intelligence , mathematics , regression analysis , software , machine learning , software system , software construction , geometry , programming language , biochemistry , chemistry , gene
Regression testing is a maintenance activity that is performed to ensure the validity of modified software. The activity takes a lot of time to run the entire test suite and is very expensive. Thus it becomes a necessity to choose the minimum set of test cases with the ability to cover all the faults in minimum time. The paper presents a new test case reduction hybrid technique based on Genetic algorithms(GA) and bee colony optimization (BCO) .GA is an evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. BCO is a swarm intelligence algorithm. The proposed approach adopts the behavior of bees to solve the given problem. It proves to be optimistic approach which provides optimum results in minimum time.