![]() Our research relies upon eye tracking hardware and software to collect and analyze the eye gazes. Proper understanding can help us to inform inexperienced coders to efficiently and accurately approach, discover, and mitigate security flaws. Our objective is to gain insight into the strategies and techniques from both novice and experienced developers. Their behavior to analyze source code and develop mitigation techniques is not well understood. Secure coders' experiences and performances vary greatly and any missed security flaws in source code may lead to costly consequences. We propose additional visualization techniques to help researchers investigate behavior of software developers' while they work software coding tasks. Finally, we discuss the limitations of current visualization methods, specifically for user controlled dynamic stimuli consisting of existing source code stimuli. Our decision matrix maps objectives for software programming to analysis techniques for comparing the eye gazes among software developers'. We propose an eye tracking design and analysis framework, which break down the various stages of software coding that require using different programming skills. Our systematic literature survey focuses on published methods for multiple types of static and dynamic changing eye tracking stimuli, especially techniques that use multiple participant-editable types of stimuli presented at once to allow for a realistic coding experience. A software developer tasks, and individual actions create complexity to design eye tracking experiments and to analyze the eye gazes. A beneficial insight is to visualize the eye gazes that capture coding behavior. One accurate and objective approach is to utilize eye tracking techniques to capture how developers' write code, use tools, and read natural language documents and instructions. The coding behavior of a software developer is not easily gathered and investigated. A common thread is that decisions that were taken in an effort to avoid one threat to validity may pose a larger threat than the one they removed. In order to promote the development and use of sound experimental methodology, we discuss both considerations which need to be applied and potential problems that might occur, with regard to the experimental subjects, the code they work on, the tasks they are asked to perform, and the metrics for their performance. ![]() In addition, myriad different factors can influence the outcome, and seemingly small nuances may be detrimental to the study’s validity. But it is hard to conduct controlled experiments with human developers, and we also need to find a way to operationalize what “comprehension” actually means. The prevailing approach for such studies is to use controlled experiments, where the difference between treatments sheds light on factors which affect comprehension. As a result, studying code comprehension is also hard. Understanding program code is a complicated endeavor. Our tool helped to analyze the difference between the strategic program comprehension of programmers based on their demographic background, time taken to complete the task, choice of programming task, and expertise. We illustrate the usefulness of our tool by applying it to the eye movements of 216 programmers of multiple expertise levels that were collected during two code comprehension tasks. The tool supports interaction techniques like filter functions, aggregations, data sampling, and many more. It can be inspected in combination with overlaid scanpaths in which the saccades can be visually encoded in several forms, including straight, curved, and orthogonal lines, modifiable by interaction techniques. The source code is shown as a visual stimulus. Hence the focus of this work is on code and program comprehension. This paper proposes an open source visual analytics tool consisting of several views and perspectives on eye movement data collected during code reading tasks when writing computer programs.
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