Exploring Eye Tracking Data on Source Code via Dual Space Analysis

Eye tracking is a frequently used technique to collect data capturing users\u27 strategies and behaviors in processing information. Understanding how programmers navigate through a large number of classes and methods to find bugs is important to educators and practitioners in software engineering. However, the eye tracking data collected on realistic codebases is massive compared to traditional eye tracking data on one static page. The same content may appear in different areas on the screen with users scrolling in an Integrated Development Environment (IDE). Hierarchically structured content and fluid method position compose the two major challenges for visualization. We present a dual-space analysis approach to explore eye tracking data by leveraging existing software visualizations and a new graph embedding visualization. We use the graph embedding technique to quantify the distance between two arbitrary methods, which offers a more accurate visualization of distance with respect to the inherent relations, compared with the direct software structure and the call graph. The visualization offers both naturalness and readability showing time-varying eye movement data in both the content space and the embedded space, and provides new discoveries in developers\u27 eye tracking behaviors. Adviser: Hongfeng Y

AN EYE TRACKING REPLICATION STUDY OF A RANDOMIZED CONTROLLED TRIAL ON THE EFFECTS OF EMBEDDED COMPUTER LANGUAGE SWITCHING

The use of multiple programming languages (polyglot programming) during software development is common practice in modern software development. However, not much is known about how the use of these different languages affects developer productivity. The study presented in this thesis replicates a randomized controlled trial that investigates the use of multiple languages in the context of database programming tasks. Participants in our study were given coding tasks written in Java and one of three SQL-like embedded languages: plain SQL in strings, Java methods only, a hybrid embedded language that was more similar to Java. In addition to recording the online questionnaire responses and the participants\u27 solutions to the tasks, the participants\u27 eye movements were also recorded using an eye tracker. Eye tracking as a method for software development studies has grown in recent years and allows for finer-grain information about how developers complete programming tasks. Eye tracking data was collected from 31 participants (from both academia and industry) for each of the six programming tasks they completed. Unlike the original study, we were unable to find a significant effect on productivity due to the language used or whether they were a native English speaker. However, we did find the same effect of participant experience on programming productivity which indicates that more experienced programmers are able to complete polyglot programming tasks in a more efficient manner. We also found that all participants looked at the sample code the same percentage of the time for a given task regardless of their experience or language variant they were given. The top level navigation behavior also remained largely unchanged across experience or language variants. We found that professionals performed more transitions in the code between the Java code and method parameters than their novice counterparts. Overall, we found that the level of polyglot programming did not have as significant of an effect as the task itself. The high-level strategy that participants employed appeared similar regardless of language variant they were given. Adviser: Bonita Shari

Exploring Eye Tracking Data on Source Code via Dual Space Analysis

Eye tracking is a frequently used technique to collect data capturing users\u27 strategies and behaviors in processing information. Understanding how programmers navigate through a large number of classes and methods to find bugs is important to educators and practitioners in software engineering. However, the eye tracking data collected on realistic codebases is massive compared to traditional eye tracking data on one static page. The same content may appear in different areas on the screen with users scrolling in an Integrated Development Environment (IDE). Hierarchically structured content and fluid method position compose the two major challenges for visualization. We present a dual-space analysis approach to explore eye tracking data by leveraging existing software visualizations and a new graph embedding visualization. We use the graph embedding technique to quantify the distance between two arbitrary methods, which offers a more accurate visualization of distance with respect to the inherent relations, compared with the direct software structure and the call graph. The visualization offers both naturalness and readability showing time-varying eye movement data in both the content space and the embedded space, and provides new discoveries in developers\u27 eye tracking behaviors. Adviser: Hongfeng Y