Simplifying Software Metric Models via Hierarchical LASSO with Incomplete Data Samples
Tianfa Xie, W. Eric Wong, Wenxing Ding · 2010
Software metric models can be used in predicting the interested target software metric(s) for future software project based on certain related metric(s). However, during the construction of such a model, incomplete data often appear in data sample gained from analogous past projects. In addition, whether a particular continuous predictor metric or a particular category for a certain categorical predictor metric should be included in the model must be determined in practice. To solve these problems, this paper introduces a methodology integrating the k-nearest neighbors (k-NN) multiple imputation method, kernel smoothing, Monte Carlo simulation, and a latest variable selection method. Thus, a more flexible model is constructed. A case study is given to illustrate the proposed procedures.