design and implementation of software effort estimation models using regression techniques

Abstract

Estimation of effort for the proposed software is a standout amongst the most essential activities in project management. Proper estimation of effort is often desirable in order to avoid any sort of failures in a project and is the practice to adopt by developers at the very beginning stage of the software development life cycle. This research proposed a novel model to predict software size and effort from use case diagrams. The main advantage of the proposed model is that it can be used in the early stages of the software life cycle, and that can help project managers efficiently conduct cost estimation early, thus avoiding project overestimation and late delivery among other benefits. Software size, productivity, complexity and requirements stability are the inputs of the model. In order to create results of estimation with more accuracy, when managing issues of complex connections in the middle of inputs as well as yields, and where, there is a distortion in the inputs by high noise levels, the application of regression techniques helps to bring out results with more accuracy. The research work carried out here presents the use of various regression techniques for software effort estimation using UCP approach. In our research, we found that non functional requirements (NFR) can influence the software effort estimation. In our model, we represent NFR through three main factors, which include productivity, complexity and requirements uncertainty. An estimation model predictive accuracy is determined by the difference of the various accuracy measures. The one with minimum relative error is considered to be the best fit. The model predictive accuracy is needed to be statistically significant in order to be the best fit. Models predictive accuracy indicators statistically tested before taking a decision to use a model for estimation. Then, the techniques; Analysis Of Variance ANOVA and regression to form Least Square (LS) set and Estimation by Analogy (EbA) set were used.