Hybrid Approach for Optimizing Test Suite Based on GA & ACO
Author(s):
Khushboo Arora, Mamta Arora
Keywords:
Ant Colony Optimization, Genetic Algorithm, Optimization, Software Testing, Test Suite.
Abstract
Nowadays, Software testing is the most important part of a successful software product. It is a process with the intent of detecting as many errors in the software process. Software testing takes an input which executes the process and then produces an output. This output mainly depends upon the software testing. The quality and stability of a software is analyzed using software testing and is achieved by suitable test suite. Manual testing is a very difficult and expensive process and also takes a lot of time. The main problem of manual testing is the problem of code coverage, which is not performed at a regular interval. Thus there is a necessity to choose the best and minimized test suite which maximizes the fault coverage in minimum time. The paper presents a new hybrid approach for optimizing the software test suite by combining two main algorithms: Genetic algorithm and Ant Colony Optimization. Genetic algorithm, an optimization algorithm is based on natural evolution, which optimizes the solutions using different operators such as selection, crossover and mutation whereas Ant Colony Optimization algorithm is a meta-heuristic technique. The proposed methodology adopts the behaviour of ants and applies some genetic operator i.e. crossover operator to solve a problem. The paper also provides a comparison of the above hybrid technique with Genetic Algorithm and Ant Colony Optimization based on the number of test cases.
Article Details
Unique Paper ID: 143798

Publication Volume & Issue: Volume 3, Issue 2

Page(s): 65 - 69
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