Web Effort and Defect Prediction using Bayseian Model

Abstract

objective. The objective of the paper is the web effort prediction how effectively the web application project will be developed and
what are all the defects, quality control also developed in the web application projects by using the COCOMO and software effort techniques are
also been involved. In the project how effectively those projects will be developed. The approach allows us to incorporate causal process factors
as well as combine qualitative and quantitative measures, hence overcoming some of the well-known limitations of traditional software metrics
methods. What are all the effort prediction is applied and what are all the defects present in that by using the Bayesian network (BN) model and
defect prediction using the “McCabe’s versus Halstead versus lines of code counts” for generating defect predictors. By suing this method can
easily find the defect prediction in which modules the most errors are occurring. We show here that such debates are irrelevant since how the
attributes are used to build predictors is much more important than which particular attributes are used. Also, contrary to prior pessimism, we
show that such defect predictors are demonstrably useful.