%0 Journal Article
	%A Pradeep Singh and  Shrish Verma
	%D 2015
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 99, 2015
	%T Cross Project Software Fault Prediction at Design Phase
	%U https://publications.waset.org/pdf/10001596
	%V 99
	%X Software fault prediction models are created by using
the source code, processed metrics from the same or previous version
of code and related fault data. Some company do not store and keep
track of all artifacts which are required for software fault prediction.
To construct fault prediction model for such company, the training
data from the other projects can be one potential solution. Earlier we
predicted the fault the less cost it requires to correct. The training
data consists of metrics data and related fault data at function/module
level. This paper investigates fault predictions at early stage using the
cross-project data focusing on the design metrics. In this study,
empirical analysis is carried out to validate design metrics for cross
project fault prediction. The machine learning techniques used for
evaluation is Naïve Bayes. The design phase metrics of other projects
can be used as initial guideline for the projects where no previous
fault data is available. We analyze seven datasets from NASA
Metrics Data Program which offer design as well as code metrics.
Overall, the results of cross project is comparable to the within
company data learning.
	%P 800 - 805