Software Maintenance Effort Estimation – Neural Network Vs Regression Modeling Approach

The global IT industry has now matured. As more and more systems grow old and enter into the maintenance stage, software maintenance (SM) is becoming one of the most carried out and challenging tasks. Besides, the industry is also facing a shift in traditional technical environment by way of use of newer tools and approaches of software development, migration from legacy software to current software and dynamic changes in the SM environment. The challenge then lies in accurately modeling and predicting the SM effort, schedule and risk involved, under the above circumstances. This work employs a neural network (NN) approach to model and predict the software maintenance effort based on an available real life dataset of outsourced maintenance projects (Rao and Sarda, 36 projects of 14 drivers). A comparison between results obtained by NN and regression modeling is also presented. It is concluded that NN is able to successfully model the complex, non-linear relationship between a large number of effort drivers and the software maintenance effort, with results closely matching the effort estimated by experts.