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Software effort estimation accuracy is a key factor in effective planning, controlling, and delivering 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. The researchers and practitioners are striving to identify which machine learning estimation technique gives more accurate results based on evaluation measures, datasets and 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 software development. In this article, the performance of the machine learning ensemble and solo techniques are investigated on publicly and non-publicly domain datasets based on the 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 (QA) criteria, extracting data, and drawing results. We have evaluated a state-of-the-art accuracy performance of 35 selected studies (17 ensemble, 18 solo) using mean magnitude of relative error 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.

Abstract-Despite the fact that a handover process is just as frequently performed as any development process, little is known about it. Still, it is regarded as one of the lifecycle processes that is not well explored and defined. In this paper, we study the handover process within eighteen companies with the purpose of demarcating its scope within software lifecycle. Our goal is to find out how industry understands handover process and how it places it within software lifecycle. As a result, we have identified seven different scope contexts for the handover process. We have also provided evidence of its wide lifecycle span and its overlap with development, predelivery and postdelivery maintenance processes.

Abstract-Handing over a software system from development to maintenance is still an under-researched domain. The software community has a hazy insight into its constellation and inherent activities. In this paper, we have evaluated a preliminary version of a taxonomy of handover activities within one Swedish software company. The evaluation is conducted in an in-house handover context only. Despite this, our results provide evidence of its enormous complexity, variability and strong dependency on many other software engineering processes.

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