Software Defect Prediction System using Multilayer Perceptron Neural Network with Data Mining
Gayathri M1, A. Sudha2
1Gayathri M, II year student, M.E(CSE) Department of CSE, Al-Ameen Engineering College, College, Erode, (Tamil Nadu), India.
2A. Sudha, Assistant Professor, Department of CSE, Al-Ameen Engineering Erode, (Tamil Nadu), India.
Manuscript received on 20 May 2014 | Revised Manuscript received on 25 May 2014 | Manuscript published on 30 May 2014 | PP: 54-59 | Volume-3 Issue-2, May 2014 | Retrieval Number: B1100053214/2014©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Fault prediction in software systems is crucial for any software organization to produce quality and reliable software. Faults (defects) or fault-proneness of software modules are to be predicted in the early stages of software life cycle, so that more testing efforts can be put on faulty modules. Various metrics in software like Cyclomatic complexity, Lines of Code have been calculated and effectively used for predicting faults. Techniques like statistical methods, data mining, machine learning, and mixed algorithms, which were based on software metrics associated with the software, have also been used to predict software defects. Many works have been carried out in the prediction of faults and fault-proneness of software systems using varied techniques. In this paper, an enhanced Multilayer Perceptron Neural Network based machine learning technique is explored and a comparative analysis is performed for the modeling of fault-proneness prediction in software systems. The data set of software metrics used for this research is acquired from NASA’s Metrics Data Program (MDP).
Keywords: Faults, Fault-proneness, Software Metrics, Software Defect Prediction, Multilayer Perceptron Neural Network.
Scope of the Article: Computer Network