On the Impact of Layout Quality to Understanding UML Diagrams: Not Just Pretty Pictures.
Harald Störrle · Software Engineering & Management · 2015
In a string of empirical studies, we show that the layout of UML diagrams contributes significantly to understanding the underlying model. This effect extends over different factors such as diagram type & size, and expertise. Status Quo The Unified Modeling Language (UML) has been called the “lingua franca of software engineering” for over a decade now. It is widely believed that, to a sizable degree, its popularity is rooted in the predominantly visual nature of UML models. The advantage of visual over textual notations is, generally speaking, that they support human perceptual and thought processes, making diagrams a more cognitively efficient medium than, say, prose. Practical experience suggests that the usage and understanding of UML diagrams is greatly affected by the quality of their layout. While existing research failed to provide conclusive evidence in support of this hypothesis, our own work [Sto11, Sto12, Sto14] provided substantial evidence to this effect. Size Matters When analyzing the impact factors, we find that diagram size is an important factor to diagram understanding; this is consistent with previous findings [MRC07]. Other factors like expertise level are important, too, though to a lesser degree, and some factors appear to be irrelevant, such as diagram type. Since there was no adequate definition of this notion, we had to defined diagram size metrics first. It turns out that the most trivial notion of simply counting diagrammatic elements is highly correlated to more complex notions, an effect known from program size metrics. By Occams razor, thus, we conclude that the size of a diagram should be measured as the number of diagram elements (i.e., geometric shapes, annotations, and line segments). Studying the impact of diagram size to diagram understanding by modelers, we find that there is a strong negative correlation between size and performance as well as preference. Our results are statistically highly significant and far exceed earlier work in terms of validity in several dimensions. We utilize these results to derive recommendations on diagram sizes that are optimal for model understanding. More recent work has begun to uncover the cognitive mechanisms involved in the understanding of UML diagrams [Mai14, SBCM14].