Prediction of Software Effort by Using Non-Linear Power Regression for Heterogeneous Projects Based on Use case Points and Lines of code

Abstract

Software cost estimation is necessary for all software organizations for contract negotiation. Numerous researchers have worked over the last four decades to provide estimation models, which can estimate efforts as accurately as they could, but the continuous change in the development models and use of new programming languages requires the development of new techniques for effort estimation. The size of the project must be estimated to determine the effort involved, which is rely on the programming languages and techniques employed for its development. Use case points, object points, lines of code, and story points are a few of the size matrices that are used for estimation using algorithmic models. There are several estimating techniques for the estimation of algorithmic models for various development approaches, but none of them offers a unified model for effort estimation for procedural and object-oriented projects. This work identifies common parameters for object-oriented and procedural-oriented programming languages. After the identification of common parameters, a combined model is developed for the estimation of effort for object-oriented and procedural-oriented programming language projects. The model's input is the size, which is measured in use case points and lines of source code. Power regression analysis of non-linear regression is applied to design the proposed model is designed using. The identified common parameters are multiplied with the mathematical equation achieved by power regression analysis to estimate the development effort for heterogeneous projects. The proposed work is assessed with the mean magnitude of relative error (MMRE). The error calculated with the proposed model is -0.16. The values of MMRE are further compared with the existing techniques such as linear regression, case-based reasoning, ensemble model, radial basis function neural network, and fuzzy analytic hierarchy process, and it was perceptible from the result that the proposed model delivers minimum error. The proposed work delivers the highest accuracy for software project estimation with the least amount of error.