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.　©　2010　IEEE.