Toward a Smell-aware Bug Prediction Model

Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact
of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components
affected by code smells. In this paper, we capture previous findings on bug-proneness to build a specialized bug prediction model for
smelly classes. Specifically, we evaluate the contribution of a measure of the severity of code smells (i.e., code smell intensity) by adding
it to existing bug prediction models based on both product and process metrics, and comparing the results of the new model against the
baseline models. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor.
We also compare the results achieved by the proposed model with the ones of an alternative technique which considers metrics about
the history of code smells in files, finding that our model works generally better. However, we observed interesting complementarities
between the set of buggy and smelly classes correctly classified by the two models. By evaluating the actual information gain provided
by the intensity index with respect to the other metrics in the model, we found that the intensity index is a relevant feature for both
product and process metrics-based models. At the same time, the metric counting the average number of code smells in previous
versions of a class considered by the alternative model is also able to reduce the entropy of the model. On the basis of this result,
we devise and evaluate a smell-aware combined bug prediction model that included product, process, and smell-related features.
We demonstrate how such model classifies bug-prone code components with an F-Measure at least 13% higher than the existing
state-of-the-art models.