Comparing Local and Global Software Effort Estimation Models -- Reflections on a Systematic Review

Stephen G. MacDonell, M. Shepperd
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引用次数: 54

Abstract

The availability of multi-organisation data sets has made it possible for individual organisations to build and apply management models, even if they do not have data of their own. In the absence of any data this may be a sensible option, driven by necessity. However, if both cross-company (or global) and within-company (or local) data are available, which should be used in preference? Several research papers have addressed this question but without any apparent convergence of results. We conduct a systematic review of empirical studies comparing global and local effort prediction systems. We located 10 relevant studies: 3 supported global models, 2 were equivocal and 5 supported local models. The studies do not have converging results. A contributing factor is that they have utilised different local and global data sets and different experimental designs thus there is substantial heterogeneity. We identify the need for common response variables and for common experimental and reporting protocols.
比较局部和全局软件工作量估算模型——对系统回顾的思考
多组织数据集的可用性使得单个组织建立和应用管理模型成为可能,即使它们没有自己的数据。在没有任何数据的情况下,出于必要性,这可能是一个明智的选择。但是,如果跨公司(或全球)和公司内部(或本地)数据都可用,应该优先使用哪个?一些研究论文已经解决了这个问题,但没有任何明显的结果趋同。我们对比较全球和地方努力预测系统的实证研究进行了系统回顾。我们找到了10项相关研究:3项支持全球模型,2项模棱两可,5项支持局部模型。这些研究的结果并不一致。一个促成因素是,他们使用了不同的本地和全球数据集以及不同的实验设计,因此存在实质性的异质性。我们确定需要共同的响应变量和共同的实验和报告协议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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