An Ann Based Approach For Software Fault Prediction Using Object Oriented Metrics

Rajdeep Kaur , Sumit sharma .

ISBN 10: 1723893676 / ISBN 13: 9781723893674

Published by Independently published, 2018

About this Item

57 pages. 10.00x8.00x0.15 inches. In Stock.

Seller Inventory # zk1723893676

Bibliographic Details

• Title: An Ann Based Approach For Software Fault Prediction Using Object Oriented Metrics
• Publisher: Independently published
• Publication Date: 2018
• Language: English
• ISBN 10: 1723893676
• ISBN 13: 9781723893674
• Binding: Paperback
• Condition: Brand New
• Weight: 0.18 kilograms

About this title

Synopsis

During recent years, the enormous increase in demand for software products has been experienced. High quality software is the major demand of users. Predicting the faults in early stages will improve the quality of software and apparently reduce the development efforts or cost. Fault prediction is majorly based on the selection of technique and the metrics to predict the fault. Thus metrics selection is a critical part of software fault prediction. Currently techniques been evaluated based on traditional set of metrics. There is a need to identify the different techniques and evaluate them on the bases of appropriate metrics. In this research, Artificial neural network based SFP model is designed. The ANN model is trained using Levenberg Marquardt (LM) Algorithm For classification task, ANN is one of the most effective technique. Prediction is performed on the basis of object-oriented metrics. 5 object oriented metrics . are selected as input parameter from CK and Martin metric sets are selected as input parameters. DIT(Depth of inheritance tree, RFC(Response for class), WMC (weíghted methods per class), Ca (Afferent coupling), CBO (couplíng between objects) are the metrics used in this study. The experiments are performed on 18 public datasets from PROMISE repository. Receiver operating characteristic curve, accuracy, and Mean squared error are taken as performance parameters for the prediction task. The results of the proposed systems signify that ANN provides significant results in terms of accuracy and error rate.