No abstract

Many visualization techniques are used by software engineers to understand and to analyze the static structure of software systems, with the static structure being extracted from the source code. However, the need for scalable visualizations, which take into account the increasing number of code entities (classes and interfaces) and different types of code couplings (relations between them) in software systems, is still not sufficiently fulfilled. In this paper, we extend the design of an existing multimatrix visualization approach to represent the static structure of software systems in a scalable way. First, we extended the data model and the algorithms. Second, we added more visualization and interaction elements. Finally, we incorporated the folding (collapsing) and the unfolding (expanding) of the package hierarchy, which have quadratic time complexity and quadratic space complexity in the number of nodes. This extended approach can be applied to support program comprehension and to analyze the static structure of software systems.

Software systems are often modeled and visualized as graphs in order to understand their higher-level structure: code entities are connected by dependencies or couplings. However, when only considering one type of code coupling such as method calls, the understanding gained stays limited to this specific aspect. Encoding multiple types of code coupling in the graph promises to broaden the understanding. Different approaches already exist for visually discerning those types in graph diagrams. In this paper, we study two of these techniquesa node-link and a matrix approach-in a realistic scenario where the classes and interfaces of a system are connected by six different types of code coupling. The explorative user study that we conducted with interactive versions of the two visualizations focuses on getting an insight on how software developers use the visualizations for understanding an unknown system. We classified typical visual structures that the participants were able to identify and connected these structures to software engineering problems. Despite the fundamental difference in approach, the participants identified the same graph structures targeting similar tasks with both visualizations.

Supplementary data are available at Bioinformatics online.

BackgroundHistone modifications play an important role in gene regulation. Their genomic locations are of great interest. Usually, the location is measured by ChIP-seq and analyzed with a peak-caller. Replicated ChIP-seq experiments become more and more available. However, their analysis is based on single-experiment peak-calling or on tools like PePr which allows peak-calling of replicates but whose underlying model might not be suitable for the conditions under which the experiments are performed.ResultsWe propose a new peak-caller called ‘Sierra Platinum’ that allows peak-calling of replicated ChIP-seq experiments. Moreover, it provides a variety of quality measures together with integrated visualizations supporting the assessment of the replicates and the resulting peaks, as well as steering the peak-calling process.ConclusionWe show that Sierra Platinum outperforms currently available methods using a newly generated benchmark data set and using real data from the NIH Roadmap Epigenomics Project. It is robust against noisy replicates.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1248-6) contains supplementary material, which is available to authorized users.