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.

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机器学习技术的软件工作量估计精度预测：系统性能评估

软件工作量估算的准确性是在预算和时间表内有效规划、控制和交付成功软件项目的关键因素。高估和低估都是未来软件开发的关键挑战，因此对软件工作量估计的准确性有持续的需求。研究人员和从业人员正在努力根据评估措施、数据集和其他相关属性，确定哪种机器学习估计技术能够提供更准确的结果。相关研究的作者通常不知道以前发表的机器学习工作量估计技术的结果。本研究的主要目的是帮助研究人员了解哪种机器学习技术可以在软件开发中产生有希望的工作量估计准确度预测。在本文中，基于两个最常用的准确性评估指标，在公开和非公开领域数据集上研究了机器学习集成和单独技术的性能。我们使用了 Kitchenham 和 Charters 提出的系统文献综述方法。这包括搜索最相关的论文、应用质量评估 (QA) 标准、提取数据和绘制结果。我们评估了 35 项选定研究（17 个集合，18 solo) 使用平均相对误差幅度和 PRED (25) 作为一组可靠的准确度指标，用于评估两种技术之间的准确度性能，以报告本研究中所述的研究问题。我们发现机器学习技术是集成工作量估计 (EEE) 技术构建中最常用的技术。这项研究的结果表明，EEE 技术通常比单独技术产生有希望的估计精度。