Abstract
Project management is an essential step for the success of any software project. One of the most significant tasks in software project management is estimating the cost and effort of software development at the start of the project. The primary purpose of this research is to study the impact of tuning the base learner hyperparameter with different machine learning/ensemble models to improve estimator accuracy. We used random forest, support vector regression, and elastic net as the base learners. In this study, Albrecht, Desharnais, and China datasets were used for experimentation. We also performed feature selection and considered only those features that have strong correlation with target feature, i.e., effort. The mean magnitude relative error (MMRE) and PRED(25) results demonstrate that utilizing elastic net as the base learner for AdaBoost outperforms the other models.
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Jawa, M., Meena, S. (2023). Comparative Analysis of Ensemble Models for Software Effort Estimation. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 522. Springer, Singapore. https://doi.org/10.1007/978-981-19-5292-0_5
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