Stable Voronoi-based visualizations for software quality monitoring

Abstract

Voronoi treemaps can be used to effectively visualize software quality attributes of a given software system. To easily keep track of how the values of such attributes change over time as the software evolves from one version to the next, it is essential that the produced visualizations are stable in the sense that small changes to the values of the attributes lead to small changes in the visualization. Due to the non-deterministic nature of the standard algorithm for computing Voronoi treemaps, stability is not guaranteed. In this paper we introduce stable Voronoi treemaps for additively weighted power Voronoi diagrams. We employ scaled Hilbert curves to place Voronoi sites in the plane, thereby achieving a predictable first good approximation of the final location of the sites, and then retain the order in which sites are placed along that curve for easy comparison with revisions of the same software system. We show that our algorithm not only provides more stability, but also that because of better placement it needs fewer iterations to compute its result.