Detecting Bad Smells in Software Systems with Linked Multivariate Visualizations

Parallel coordinates plots and RadViz are two visualization techniques that deal with multivariate data. They complement each other in identifying data patterns, clusters, and outliers. In this paper, we analyze multivariate software metrics linking the two approaches for detecting outliers, which could be the indicators for bad smells in software systems. Parallel coordinates plots provide an overview, whereas the RadViz representation allows for comparing a smaller subset of metrics in detail. We develop an interactive visual analytics system supporting automatic detection of bad smell patterns. In addition, we investigate the distinctive properties of outliers that are not considered harmful, but noteworthy for other reasons. We demonstrate our approach with open source Java systems and describe detected bad smells and other outlier patterns.