Bad-smell prediction significantly impacts on software
quality. It is beneficial if bad-smell prediction can be performed
as early as possible in the development life cycle. We present
methodology for predicting bad-smells from software design
model. We collect 7 data sets from the previous literatures which
offer 27 design model metrics and 7 bad-smells. They are learnt
and tested to predict bad-smells using seven machine learning
algorithms. We use cross-validation for assessing the
performance and for preventing over-fitting. Statistical
significance tests are used to evaluate and compare the prediction
performance. We conclude that our methodology have proximity
to actual values.