Intelligent Method for Cost Estimation during Software Maintenance

Abstract

Software maintenance is very important and time consuming task which require a lot of parameters to be taken care during the whole cycle of maintenance of software. The software maintenance engineer has used various cost estimation models in their research work like COCOMO-I, COCOMO-II, SLIM, SEER-SEM and FP. The problem with these models is that they require the complete data and information making any decision and benchmark. Therefore the cost of software maintenance varies with types and complexity of the software under the maintenance. We have identified the various factors which effects the cost of the software maintenance. The various cost estimation models require a lots of metrics and only after calculating the complete parameters, then only the software cost can be determined.To reduce the software maintenance cost the proposed model has been introduce which can overcome the cost even there is missing information. The Probability factor (PF) method has been used which can overcome the cost. The proposed model used the various factors as input and accordingly the probability of each factors has been calculated. Some of quality factor (in different software) can't be computed  Sometimes cost of compute all of quality factors are very high  We need long time for compute all of quality factors  In some cases we need to a lot of people (user, quality manager, member of quality assurance's team) to compute quality factors Some other researchers have also deal with uncertainty in quality factor and matrices. Motameni et al. (2010) proposed a model for software quality with BNs and ISO9126 quality model. There model doesn't have last quality model's problems and can predict software quality with incomplete and uncertain data. Also by this model the time and cost of calculating software quality is reduced.