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This paper describes the results of a large (+ 1100 responses) survey of professional software developers concerning standards for naming source code methods. The various standards for source code method names are derived from and supported in the software engineering literature. The goal of the survey is to determine if there is a general consensus among developers that the standards are accepted and used in practice. Additionally, the paper examines factors such as years of experience and programming language knowledge in the context of survey responses. The survey results show that participants very much agree about the importance of various standards and how they apply to names and that years of experience and the programming language has almost no effect on their responses. The results imply that the given standards are both valid and to a large degree complete. The work provides a foundation for automated method name assessment during development and code reviews. 

Program comprehension is a vital skill in software development. This work investigates program comprehension by examining the eye movement of novice programmers as they gain programming experience over the duration of a Java course. Their eye movement behavior is compared to the eye movement of expert programmers. Eye movement studies of natural text show that word frequency and length influence eye movement duration and act as indicators of reading skill. The study uses an existing longitudinal eye tracking dataset with 20 novice and experienced readers of source code. The work investigates the acquisition of the effects of token frequency and token length in source code reading as an indication of program reading skill. The results show evidence of the frequency and length effects in reading source code and the acquisition of these effects by novices. These results are then leveraged in a machine learning model demonstrating how eye movement can be used to estimate programming proficiency and classify novices from experts with 72% accuracy. 

Eye tracking tools are used in software engineering research to study various software development activities. However, a major limitation of these tools is their inability to track gaze data for activities that involve source code editing. We present a novel solution to support eye tracking experiments for tasks involving source code edits as an extension of the iTrace community infrastructure. We introduce the iTrace-Atom plugin and gazel—a Python data processing pipeline that maps gaze information to changing source code elements and provides researchers with a way to query this dynamic data. iTrace-Atom is evaluated via a series of simulations and is over 99% accurate at high eye-tracking speeds of over 1,000Hz. iTrace and gazel completely revolutionize the way eye tracking studies are conducted in realistic settings with the presence of scrolling, context switching, and now editing. This opens the doors to support many day-to-day software engineering tasks such as bug fixing, adding new features, and refactoring. 

Studies of eye movements during source code reading have supported the idea that reading source code differs fundamentally from reading natural text. The paper analyzed an existing data set of natural language and source code eye movement data using the E-Z reader model of eye movement control. The results show that the E-Z reader model can be used with natural text and with source code where it provides good predictions of eye movement duration. This result is confirmed by comparing model predictions to eye-movement data from this experiment and calculating the correlation score for each metric. Finally, it was found that gaze duration is influenced by token frequency in code and in natural text. The frequency effect is less pronounced on first fixation duration and single fixation duration. An eye movement control model for source code reading may open the door for tools in education and the industry to enhance program comprehension. 

The evolution and effort in designing and implementing iTrace, an infrastructure for integrating eye tracking into developer environments, is presented. The goal is to make eye tracking practical for various stakeholders in software engineering namely researchers, practitioners, and educators. An overview of iTrace and the general process involved in conducting an eye tracking study with human subjects using iTrace is presented in this tool demo paper. Upcoming features and ongoing plans for community involvement are also presented.