Performance Comparison of a Software Fault Prediction System using Fuzzy C-Means Clustering Approach and a Hybrid Technique (Combination of Fuzzy C-Means and Particle Swarm Optimization)

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.4 · Issue 11

Published

June 2016

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Open Access

Licence

CC BY-NC-SA 4.0

Dazy Arya Mr. Aishwary Kulshrestha

Early detection of fault prone software components enables verification experts to concentrate their time and resources on the problem areas of the software systems under development. In this paper, performance comparison of a Software Fault Prediction System using Fuzzy c-means clustering approach and a hybrid technique (Combination of Fuzzy c-means and Particle Swarm Optimization) a has been performed with the real time data set named PC1, taken from NASA MDP software projects.

Dazy Arya, Mr. Aishwary Kulshrestha (2016). Performance Comparison of a Software Fault Prediction System using Fuzzy C-Means Clustering Approach and a Hybrid Technique (Combination of Fuzzy C-Means and Particle Swarm Optimization). International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.4, Issue 11. ISSN: 2319-4863.

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