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A comparative study of attribute weighting heuristics for effort estimation by analogy

机译:类比估算工作量的属性加权启发式方法的比较研究

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Five heuristics for attribute weighting in analogy-based effort estimation are evaluated in this paper. The baseline heuristic involves using all attributes with equal weights. We propose four additional heuristics that use rough set analysis for attribute weighting. These five heuristics are evaluated over five data sets related to software projects. Three of the data sets are publicly available, hence allowing comparison with other methods. The results indicate that three of the rough set analysis based heuristics perform better than the equal weights heuristic. This evaluation is based on an integrated measure of accuracy.
机译:本文评估了基于类比的努力估计中属性加权的五种启发式方法。基准启发式涉及使用权重相等的所有属性。我们提出了四种使用粗糙集分析进行属性加权的启发式方法。在与软件项目相关的五个数据集上评估了这五种启发式方法。其中三个数据集是公开可用的,因此可以与其他方法进行比较。结果表明,三种基于粗糙集分析的启发式方法的性能要优于等权启发式方法。该评估基于准确性的综合度量。

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