The paper presents the Source Code Analysis and Lexical Annotation Runtime (SCALAR), a tool specialized for mapping (annotating) source code identifier names to their corresponding part-of-speech tag sequence (grammar pattern). SCALAR's internal model is trained using scikit-learn's GradientBoostingClassifier in conjunction with a manually-curated oracle of identifier names and their grammar patterns. This specializes the tagger to recognize the unique structure of the natural language used by developers to create all types of identifiers (e.g., function names, variable names etc.). SCALAR's output is compared with a previous version of the tagger, as well as a modern off-the-shelf part-of-speech tagger to show how it improves upon other taggers' output for annotating identifiers. The code is available on Github11https://github.com/SCANL/scanl_tagger

A significant challenge in eye-tracking studies is detecting and fixing errors in data collection that happen for various reasons (drift, calibration issues, etc.). Many errors cannot be fully mitigated and require manual correction (which is intensively time-consuming) or automated correction. The work presented in this paper focuses on error correction, primarily on eye-tracking data on source code written in programming languages such as C++, Java, and C#. Many automated correction solutions are general-purpose, computationally inefficient, and use little information about the stimulus. To bridge this gap, we introduce srcGaze , a heuristic algorithm explicitly developed for correcting fixation gaze events in eye-tracking data from studies using source code as a stimulus. A golden dataset is manually constructed and verified to establish the heuristics. Results show a ≈40% improvement compared to no fixation correction. The approach has a multi-linear complexity and can correct over 44K fixations in approximately 6 seconds.

We present Stereocode, a static analysis tool engineered to automatically identify, and re-document software systems written in C++, C#, and/or Java with method and class stereotypes. A stereotype is a simple abstraction that encapsulates the high-level behavior of a method or a class. The tool is built around the srcML infrastructure, an XML representation of source code. Stereocode annotates the srcML input with the computed stereotypes as XML attributes to the function and class tags. We showcase Stereocode's efficiency in conducting large-scale analysis of software systems, which involves using 1050 repositories from GitHub across C++, C#, and Java. The results provide valuable insights into the distribution of stereotypes. A demo video is available at: https://youtu.be/D90xwUIPbOI.

The iTrace Infrastructure is a suite of community eye-tracking tools that enables researchers to conduct eye-tracking studies on software projects in real development environments. The infrastructure consists of tools providing support for data gathering, post processing, and visualization. iTrace-Visualize provides researchers with a way to visualize gathered and post-processed eye-movement data. iTrace involves the analysis of more than just eye-movement data, and includes information gathered from the development environment and the source code. This work describes additions to iTrace-Visualize that provide researchers with visualizations of the gathered source code data. Specifically, a tokenized heatmap of the source code is presented, which shows the source code tokens that are viewed the most. Additionally, a region of interest scarf plot that details the timeline of what parts of the code a participant views is added as a new feature. A usage example comparing student and industry developers is presented to demonstrate the use of these tools. Demo Video: https://youtu.be/0iZcCC8CK94

iTrace is community infrastructure that allows software engineering researchers to conduct eye-tracking studies on large realistic code bases. The iTrace infrastructure consists of a set of tools that assist with gathering, processing, and evaluating eye-tracking data on large software projects within an Integrated Development Environment (IDE). A typical eye-tracking study results in millions of raw gazes that are overwhelming to view and sort through. To help researchers view and comprehend this data, iTrace-Visualize is presented. This tool integrates information produced by the iTrace infrastructure into a dynamic video recording of the eye-tracking session. Eye fixations and the scan path between fixations are overlayed on the video. Additionally, the line being examined can be highlighted in the video. iTrace-Visualize lets a researcher replay eye fixations via a video overlay immediately after a study. This serves as quick validation of what was done during the study and can also provide quick insights into what the participants looked at. To illustrate iTrace-Visualize's capabilities, a small preliminary study is performed. Demo Video-https://youtu.be/c1hUFDmBM50