Software Effort Estimation Accuracy Prediction of Machine Learning Techniques: A Systematic Performance Evaluation
It addresses the problem of inaccurate software effort estimation for researchers and practitioners, but it is incremental as it synthesizes existing studies rather than introducing new methods.
This study systematically evaluated the accuracy of machine learning techniques for software effort estimation, comparing ensemble and solo methods using metrics like MMRE and PRED(25) across 28 studies, and found that ensemble techniques generally provide more promising estimation accuracy than solo techniques.
Software effort estimation accuracy is a key factor in effective planning, controlling and to deliver a successful software project within budget and schedule. The overestimation and underestimation both are the key challenges for future software development, henceforth there is a continuous need for accuracy in software effort estimation (SEE). The researchers and practitioners are striving to identify which machine learning estimation technique gives more accurate results based on evaluation measures, datasets and the other relevant attributes. The authors of related research are generally not aware of previously published results of machine learning effort estimation techniques. The main aim of this study is to assist the researchers to know which machine learning technique yields the promising effort estimation accuracy prediction in the software development. In this paper, the performance of the machine learning ensemble technique is investigated with the solo technique based on two most commonly used accuracy evaluation metrics. We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, applying quality assessment criteria, extracting data and drawing results. We have evaluated a state-of-the-art accuracy performance of 28 selected studies (14 ensemble, 14 solo) using Mean Magnitude of Relative Error (MMRE) and PRED (25) as a set of reliable accuracy metrics for performance evaluation of accuracy among two techniques to report the research questions stated in this study. We found that machine learning techniques are the most frequently implemented in the construction of ensemble effort estimation (EEE) techniques. The results of this study revealed that the EEE techniques usually yield a promising estimation accuracy than the solo techniques.