  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.
