Feature importance for software development effort estimation using multi level ensemble approaches

Abstract

Feature importance strategy that substantially impacts software development effort estimation (SDEE) can help lower the dimensionality of dataset size. SDEE models developed to estimate effort, time, and wealth required to accomplish a software product on a limited budget are used more frequently by project managers as decision-support tool effort estimation algorithms trained on a dataset containing essential elements to improve their estimation accuracy. Earlier research worked on creating and testing various estimation methods to get accurate. On the other hand, ensemble produces superior prediction accuracy than single approaches. Therefore, this study aims to identify, develop, and deploy an ensemble approach feasible and practical for forecasting software development activities with limited time and minimum effort. This paper proposed a collaborative system containing a multi-level ensemble approach. The first level grabs the optimal features by adopting boosting techniques that impact the decided target; this subset features forward to the second level developed by a stacked ensemble to compute the product development effort concerning lines of code (LOC) and actual. The proposed model yields high accuracy and is more accurate than distinct models.