Comparison of Various Lacks of Cohesion Metrics
Taranjeet Kaur1, Rupinder Kaur2
1Taranjeet Kaur, Research Scholar B. Tech from Doaba Institute of Engineering and Technology University, Research area in software Engineering, Phagwara, Punjab, India.
2Rupinder Kaur, Assistant Professor, B. Tech from Baba Banda singh Bahadur Engg, Punjab, India.
Manuscript received on January 22, 2013. | Revised Manuscript received on February 13, 2013. | Manuscript published on February 28, 2013. | PP: 252-254 | Volume-2 Issue-3, February 2013. | Retrieval Number: C1085022313/2013©BEIESP
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© 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: Software engineering there are plenty of applications used for reduced complexity and improved fault prediction approaches. In this paper we study various metrics that are not very much suitable to find fault classes in software. Basically using the concept of metrics to find fault classes and reduced complexity of classes. . various techniques like linear regression, logistic regression, one way ANOVA, principal component analysis, radial basis function network, support vector machines, single layer perceptron, multilayer perceptron, error correction learning, back propagation algorithm. all these techniques are used to find faulty classes and reduced complexity in software.
Keywords: Object oriented classes, class cohesion metrics, software quality, statistical approach.