In a software development project, project management is indispensable, and effort estimation is one of the important factors on the management. To improve estimation accuracy, outliers are often removed from dataset used for estimation. However, the influence of the outliers to the estimation accuracy is not clear. In this study, we added outliers to dataset experimentally, to analyze the influence. In the analysis, we changed the percentage of outliers, the extent of outliers, variable including outliers, and location of outliers on the dataset. After that, effort was estimated using the dataset. In the experiment, the influence of outliers was not very large, when they were included in the software size metric, the percentage of outliers was 10%, and the extent of outliers was 100%.
['Kenichi Ono', 'Masateru Tsunoda', 'Akito Monden', 'Ken-ichi Matsumoto']
Influence of outliers on analogy based software development effort estimation