During the construction of a software metric model, incomplete data often appear in the data sample used for the construction. Moreover, the decision on whether a particular predictor metric should be included is most likely based on an intuitive or experience-based assumption that the predictor metric has an impact on the target metric with a statistical significance. However, this assumption is usually not verifiable "retrospectively" after the model is constructed, leading to redundant predictor metric(s) and/or unnecessary predictor metric complexity. To solve all these problems, the authors have earlier derived a methodology consisting of the k-nearest neighbors (k-NN) imputation method, statistical hypothesis testing, and a "goodness-of-fit" criterion. Whilst the methodology has been applied successfully to software effort metric models, it is applied only recently to software quality metric models which usually suffer from far more serious incomplete data. This paper documents the latter application based on a successful case study.