Abstract:
The software industry is rising gradually, it seems that there is no
end to software development. Modern era software projects are
complex and consist of many components, so change is unavoidable
in these projects. Therefore, change in the software requirements
document must be predicted at an early stage to preserve the
resources. There are a lot of requirement change prediction models
proposed in the literature and they are addressing a smaller number
of variables and including experts knowledge for analyzing the
requirement specification document. In this work, we have focused
on the small software systems which have focused few variables of
requirement change prediction model.The variables have a major
effect on the software requirements and these are analyzed by the
stakeholders, developers and experts with the questionnaire method.
Their knowledge is incorporated in the Bayesian network as
conditional probabilities of independent and dependent
variables.This is actually the proposed model for software
requirements change prediction.We used an algorithm with the
model by utilizing variable elimination method to obtain the
posterior probability of the revisions in software requirement
document. However this model can also be used for the large
software systems because it is effecent to resolve their problems
also. the The proposed model is evaluated by hypothesis testing,
sensitivity analysis and by comparing with the existing models. The
results obtained proved to be promising as it decreases the
probability of revisions in the requirement document. We obtained
the 0.42 probability for the revisions in the requirement document
so the results have improved the existing model predictions.
