Exploranative Code Quality Documents

Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope.Comment: IEEE VIS VAST 201

Projectional Editors for JSON-Based DSLs

Augmenting text-based programming with rich structured interactions has been explored in many ways. Among these, projectional editors offer an enticing combination of structure editing and domain-specific program visualization. Yet such tools are typically bespoke and expensive to produce, leaving them inaccessible to many DSL and application designers. We describe a relatively inexpensive way to build rich projectional editors for a large class of DSLs -- namely, those defined using JSON. Given any such JSON-based DSL, we derive a projectional editor through (i) a language-agnostic mapping from JSON Schemas to structure-editor GUIs and (ii) an API for application designers to implement custom views for the domain-specific types described in a schema. We implement these ideas in a prototype, Prong, which we illustrate with several examples including the Vega and Vega-Lite data visualization DSLs.Comment: To appear at VL/HCC 202

Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training

Training a state-of-the-art deep neural network (DNN) is a computationally-expensive and time-consuming process, which incentivizes deep learning developers to debug their DNNs for computational performance. However, effectively performing this debugging requires intimate knowledge about the underlying software and hardware systems---something that the typical deep learning developer may not have. To help bridge this gap, we present Skyline: a new interactive tool for DNN training that supports in-editor computational performance profiling, visualization, and debugging. Skyline's key contribution is that it leverages special computational properties of DNN training to provide (i) interactive performance predictions and visualizations, and (ii) directly manipulatable visualizations that, when dragged, mutate the batch size in the code. As an in-editor tool, Skyline allows users to leverage these diagnostic features to debug the performance of their DNNs during development. An exploratory qualitative user study of Skyline produced promising results; all the participants found Skyline to be useful and easy to use.Comment: 14 pages, 5 figures. Appears in the proceedings of UIST'2

MoleculARweb: A Web Site for Chemistry and Structural Biology Education through Interactive Augmented Reality out of the Box in Commodity Devices

Augmented/virtual realities (ARs/VRs) promise to revolutionize STEM education. However, most easy-to-use tools are limited to static visualizations, which limits the approachable content, whereas more interactive and dynamic alternatives require costly hardware, preventing large-scale use and evaluation of pedagogical effects. Here, we introduce https://MoleculARweb.epfl.ch, a free, open-source web site with interactive AR webpage-based apps that work out-of-the-box in laptops, tablets, and smartphones, where students and teachers can naturally handle virtual objects to explore molecular structure, reactivity, dynamics, and interactions, covering topics from inorganic, organic, and biological chemistry. With these web apps, teachers and science communicators can develop interactive material for their lessons and hands-on activities for their students and target public, in person or online, as we exemplify. Thousands of accesses to moleculARweb attest to the ease of use; teacher feedback attests to the utility in online teaching and homework during a pandemic; and in-class plus online surveys show that users find AR engaging and useful for teaching and learning chemistry. These observations support the potential of AR in future education and show the large impact that modern web technologies have in democratizing access to digital learning tools, providing the possibility to mass-test the pedagogical effect of these technologies in STEM education.Fil: Rodríguez, Fabio Cortés. École Polytechnique Fédérale de Lausanne; Suiza. Swiss Institute of Bioinformatics; SuizaFil: Frattini, Gianfranco. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas; ArgentinaFil: Krapp, Lucien F.. Ecole Polytechnique Federale de Lausanne; Francia. Swiss Institute of Bioinformatics; SuizaFil: Martinez Hung, Hassan. Universidad de Oriente; VenezuelaFil: Moreno, Diego Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; ArgentinaFil: Roldán, Mariana. Provincia de Córdoba. Instituto Colbert; ArgentinaFil: Salomón, Jorge Eduardo. Provincia de Buenos Aires. Escuela de Educación Técnica Nro. 4; ArgentinaFil: Stemkoski, Lee. Adelphi University; Estados UnidosFil: Traeger, Sylvain. École Polytechnique Fédérale de Lausanne; Suiza. Swiss Institute of Bioinformatics; SuizaFil: Dal Peraro, Matteo. École Polytechnique Fédérale de Lausanne; Suiza. Swiss Institute of Bioinformatics; SuizaFil: Abriata, Luciano Andres. École Polytechnique Fédérale de Lausanne; Suiza. Swiss Institute of Bioinformatics; Suiz

A Systematic Literature Review of Software Visualization Evaluation

Abstract Context: Software visualizations can help developers to analyze multiple aspects of complex software systems, but their effectiveness is often uncertain due to the lack of evaluation guidelines. Objective: We identify common problems in the evaluation of software visualizations with the goal of formulating guidelines to improve future evaluations. Method: We review the complete literature body of 387 full papers published in the SOFTVIS/VISSOFT conferences, and study 181 of those from which we could extract evaluation strategies, data collection methods, and other aspects of the evaluation. Results: Of the proposed software visualization approaches, 62 lack a strong evaluation. We argue that an effective software visualization should not only boost time and correctness but also recollection, usability, engagement, and other emotions. Conclusion: We call on researchers proposing new software visualizations to provide evidence of their effectiveness by conducting thorough (i) case studies for approaches that must be studied in situ, and when variables can be controlled, (ii) experiments with randomly selected participants of the target audience and real-world open source software systems to promote reproducibility and replicability. We present guidelines to increase the evidence of the effectiveness of software visualization approaches, thus improving their adoption rate