Optimizing Software Effort Estimation Accuracy with a Machine Learning Model

Accurately estimating the work required for soft-ware development is essential to success in today's fast-growing software business. Erroneous project estimates can result in project failures and overruns, which can have an impact on budgets, schedules, and client satisfaction. This research presents a novel strategy to overcome these obstacles: a heterogeneous en-semble model optimized for software effort estimating accuracy. To capitalize on the advantages of each approach and produce a more reliable estimation model, our suggested model integrates several modeling techniques, such as Use Case Point (U CP), Artificial Neural Network (ANN), and EJ. This method improves the accuracy of software effort estimation by combining multiple feature selection techniques with machine learning algorithms. Data quality evaluation, feature selection, handling of missing values, productivity analysis, and the transformation of nominal data are all included in the technique. UCP, ANN, and EJ algorithms are used to train the ensemble model. Its accuracy and performance are then evaluated and tested against ISBSG datasets. An extensive examination of the methods and techniques used in the heterogeneous ensemble model is given in this study. In the dynamic and demanding software business, we present a promising way to increase the accuracy of software effort estimation greatly by utilizing the diversity of modeling techniques.