TITLE

Software Fault Prediction Based on Machine Learning Techniques Using Software Metrics

AUTHOR(S)
Gupta, Deepali
PUB. DATE
October 2008
SOURCE
Journal of Digital Information Management;Oct2008, Vol. 6 Issue 5, p421
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Faults in software systems continue to be a major problem and many systems are delivered to users with excessive faults. This is been recognized that seeking out fault-prone parts of the system and targeting those parts for increased quality control and testing is an effective approach to fault reduction. Being able to estimate software faultiness before and during testing and analysis activities would greatly help software testing and analysis. Many systems are delivered to users with excessive faults. Prediction models based on software metrics, can estimate number of faults in software modules. In this paper different machine learning algorithms and neural network techniques on two different real-time software defect datasets are evaluated. The results show that when all the prediction techniques are evaluated then best algorithm for classification of the software components into faulty/fault-free systems is found to be Generalized Regression Neural Networks.
ACCESSION #
36120680

 

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