Robert Wallace

Maria Dhakal, Chia-Yi Su, Robert Wallace et al.

This paper describes an approach to improve code comments along different quality axes by rewriting those comments with customized Artificial Intelligence (AI)-based tools. We conduct an empirical study followed by grounded theory qualitative analysis to determine the quality axes to improve. Then we propose a procedure using a Large Language Model (LLM) to rewrite existing code comments along the quality axes. We implement our procedure using GPT-4o, then distil the results into a smaller model capable of being run in-house, so users can maintain data custody. We evaluate both our approach using GPT-4o and the distilled model versions. We show in an evaluation how our procedure improves code comments along the quality axes. We release all data and source code in an online repository for reproducibility.

Robert Wallace, Collin McMillan

We demonstrate EyeDoc, a tool for navigating software documentation with the use of the eyes. When programming, developers often have many windows open such as an IDE, consoles and GUIs for third-party utilities, the application under development, and a web browser for navigating documentation. Several studies have shown that the navigation among these different tasks imposes a small mental load which, over time, adds to a significant decrease in productivity. One solution to this problem is to increase "screen real estate" with larger monitors and higher resolutions, so that more information can be presented to the programmer at one time. But this solution has limits: the complexity and size of software is also increasing rapidly. In this paper, we use eye-tracking technology to build a tool for navigating documentation. All a programmer needs to do to use EyeDoc is look at the monitor containing the documentation. The tool detects when the eyes glance at different components of the documentation, and allows the programmer to navigate to those components by gazes and blinking. The programmer does not need to move his or her hands, or risk losing the context of other tasks on the desktop. We built EyeDoc as a research prototype and proof-of-concept using low-cost consumer eye-tracking hardware and our own software built as a JavaScript addition to JavaDocs. This paper describes the tool's design, use, and strategy for evaluation and future development.