%0 Journal Article
	%A Parvinder S. Sandhu and  Sunil Khullar and  Satpreet Singh and  Simranjit K. Bains and Manpreet Kaur and  Gurvinder Singh
	%D 2010
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 48, 2010
	%T A Study on Early Prediction of Fault Proneness in Software Modules using Genetic Algorithm
	%U https://publications.waset.org/pdf/10544
	%V 48
	%X Fault-proneness of a software module is the
probability that the module contains faults. To predict faultproneness
of modules different techniques have been proposed which
includes statistical methods, machine learning techniques, neural
network techniques and clustering techniques. The aim of proposed
study is to explore whether metrics available in the early lifecycle
(i.e. requirement metrics), metrics available in the late lifecycle (i.e.
code metrics) and metrics available in the early lifecycle (i.e.
requirement metrics) combined with metrics available in the late
lifecycle (i.e. code metrics) can be used to identify fault prone
modules using Genetic Algorithm technique. This approach has been
tested with real time defect C Programming language datasets of
NASA software projects. The results show that the fusion of
requirement and code metric is the best prediction model for
detecting the faults as compared with commonly used code based
model.
	%P 1891 - 1896