Traditional software development workflows, which rely primarily on physical multi-monitor setups, are effective for developing desktop and server-based applications that display output to users via monitors. However, these workflows face limitations in domains such as cyber-physical systems, particularly in robotics, where applications must interact directly with the physical environment. In such domains, digital twins offer an opportunity to visualize and debug these systems within a simulated and realistic environment.

Integrated development environments (IDEs) commonly employ a tab-based interface for displaying source code, which often poses challenges in efficient code navigation and retrieval. Previous research has proposed several novel approaches that have in common that they place code fragments on a 2D canvas and draw visual connections between them. In this paper, we investigate the extension of such interfaces to augmented reality (AR) environments. As AR allows to display information in three dimensions, the restriction to a 2D canvas for placing code fragments is not justified, and we implement it by allowing users to place code panels freely in 3D space. We call the resulting interface linked panels. We present the results of a quantitative user study conducted with 24 participants, aiming to explore whether the benefits observed for the canvas-based approach in traditional 2D screen environments can be replicated with linked panels in augmented reality. The participants were given tasks to identify and resolve two bugs in two different software projects using the traditional tab-based and the panel-based approaches in AR. To find possible explanations of our quantitative results we also conducted a qualitative analysis evaluating participants’ comments and different placement strategies of panels in the panel-based approach. Our results indicate that participants found more bugs with the tabs-version, but were equally fast with both tools. We also found that less skilled participants were faster with the tabs, while more skilled ones were faster with the panels. Although, participants experienced problems with the cluttered spatial arrangement of the panels, they preferred the panels version over the tabs version as it made better use of AR.

During software maintenance developers spend a considerable amount of time on tasks like navigating, identifying required code locations or tracing various call hierarchies. The classical tabbed interfaces, as found in modern IDEs, are not ideal for such tasks, leading to an inefficient workflow containing many context switches. Therefore, several programming environments, like Code Bubbles, were proposed to overcome these issues by allowing users to freely arrange code fragments on a canvas to make relations more explicit and better understand the codebase. Relations are made explicit using visual links or extra space between groups of code fragments. As a consequence, these approach quickly run out of screen space. In this paper, we present IDEVELOPAR, a tool to enhance code understanding in augmented reality. Due to the use of AR, a user is not restricted anymore by limited display sizes and can use the entire physical space as a workspace for placing and grouping code fragments as well as making changes to the codebase. First, we introduce the views and interactive functionalities of our tool. Next, we illustrate the usefulness of the tool by navigating an example program to locate and fix a bug. Finally, we briefly discuss the results of a cognitive walk-through using the cognitive dimension framework as well as a formative user study to identify potential usability problems. Moreover, in this study the participants also mentioned several advantages of our approach over the classical one. Furthermore, we found that over time the participants developed their own placement strategies.

Due to non-deterministic behavior and thread interleaving of concurrent programs, the debugging of concurrency and performance issues is a rather difficult and often tedious task. In this paper, we present an approach that combines statistical profiling, clustering and visualization to facilitate this task. We implemented our approach in a tool which is integrated as a plugin into a widely used IDE. First, we introduce our approach with details on the profiling and clustering strategy that produce runtime metrics and clusters of threads for source-code artifacts at different levels of abstraction (class and method) and the entire program. Next, we explain the design of our sparkline visualization which represents the clusters in situ, i.e. embedded in the program text next to the related source-code artifact in the source-code editor. More detailed information is available in separate views that also allow the user to interactively configure thread filters. In a demonstration study we illustrate the usefulness of the tool for understanding and fixing performance and concurrency issues. Finally, we report on first formative results from a small-scale user study.