TITLE:
A Framework for Software Defect Prediction Using Neural Networks
AUTHORS:
Vipul Vashisht, Manohar Lal, G. S. Sureshchandar
KEYWORDS:
Software Defect, Software Defect Prediction Model, Neural Network, Quality Management
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.8 No.8,
August
24,
2015
ABSTRACT: Despite the fact that a number of
approaches have been proposed for effective and accurate prediction of
software defects, yet most of these have not found widespread applicability.
Our objective in this communication is to provide a framework which is expected
to be more effective and acceptable for predicting the defects in multiple
phases across software development lifecycle. The proposed framework is based
on the use of neural networks for predicting defects in software development
life cycle. Further, in order to facilitate the easy use of the framework by
project managers, a software graphical user interface has been developed that
allows input data (including effort and defect) to be fed easily for predicting
defects. The proposed framework provides a probabilistic defect prediction
approach where instead of a definite number, a defect range (minimum, maximum,
and mean) is predicted. The claim of efficacy and superiority of proposed
framework is established through results of a comparative study, involving the
proposed frame-work and some well-known models for software defect prediction.