A Neural Network Approach for Randomized Unit Testing Based on Genetic Algorithm
R. Raju1, P. Subhapriya2
1R. Raju, Associate Professor , Department of Information Technology, Sri Manakula, Vinayagar Engineering College, Puducherry, India.
2P. Subhapriya, PG Student, Computer Science and Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, India.
Manuscript received on January 18, 2013. | Revised Manuscript received on February 03, 2013. | Manuscript published on February 28, 2013. | PP: 473-478 | Volume-2 Issue-3, February 2013. | Retrieval Number: C1169022313/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The goal of unit testing is to isolate each part of the program and show that the individual parts are correct. A unit test provides a strict, written contract that the piece of code must satisfy. As a result, it affords several benefits. Unit tests find problems early in the development cycle. In continuous unit testing environments, through the inherent practice of sustained maintenance, unit tests will continue to accurately reflect the intended use of the executable code in the face of any change. Depending upon established development practices and unit test coverage, up-to-the-second accuracy can be maintained. In this paper, a genetic algorithm to evolve a set of inputs. So the system called Nighthawk, which uses a genetic algorithm (GA) to find parameters for randomized unit testing that optimize test coverage. Therefore using a feature subset selection (FSS) tool to assess the size and content of the representations within the GA. The enhancement in this work is to introduce Neural network based unit testing , include some training sets for possible output and then apply the Genetic Algorithm. Therefore these results shows a better efficiency in the unit testing and reduce the test coverage.
Keywords: Unit testing, Genetic algorithm, Neural network, FSS.