Software　metric　models　are　useful　in　predicting　the　target　software　metric(s)　for　any　future　software　project　based　on　the　project＇s　predictor　metric(s).　Obviously,　the　construction　of　such　a　model　makes　use　of　a　data　sample　of　such　metrics　from　analogous　past　projects.　However,　incomplete　data　often　appear　in　such　data　samples.　Worse　still,　the　necessity　to　include　a　particular　continuous　predictor　metric　or　a　particular　category　for　a　certain　categorical　predictor　metric　is　most　likely　based　on　an　experience-related　intuition　that　the　continuous　predictor　metric　or　the　category　matters　to　the　target　metric.　However,　in　the　presence　of　incomplete　data,　this　intuition　is　traditionally　not　verifiable　＇retrospectively＇　after　the　model　is　constructed,　leading　to　redundant　continuous　predictor　metric(s)　and/or　excessive　categorization　for　categorical　predictor　metrics.　As　an　improvement　of　the　author＇s　previous　work　to　solve　all　these　problems,　this　paper　proposes　a　methodology　incorporating　the　k-nearest　neighbors　(k-NN)　multiple　imputation　method,　kernel　smoothing,　Monte　Carlo　simulation,　and　stepwise　regression.　This　paper　documents　this　methodology　and　one　experiment　on　it.　©2010　IEEE.