SENSE: software effort estimation using novel stacking ensemble learning

The volatile factors involved in software cost estimation have long been an occlusion for the software development life cycle. The inaccuracy they lead to during the estimation process has had an implacable effect on the stakeholders concerned. This can be mitigated by using machine learning algorithms to estimate the cost, which significantly reduces the volatility of the process and has more reliable results. Thus, implementing stacking on various datasets with SVR, LightGBM, K-nearest neighbours and Random Forest in level-0 and Ridge Regression in level-1 has given highly accurate results. The SENSE- Software Effort Estimation using Novel Stacking and Ensemble learning- model proposed in this study is substantiated on six datasets China, Kemerer, Albrecht, Nasa93, ISBSG and Maxwell and evaluated using MAE, RMSE, R², PRED and MMRE as evaluation metrics. We find that the proposed model displays competent performance in experimental evaluation and statistical analysis in comparison to the other studies used in the work.