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  1. null (Ed.)
    Recent research in empirical software engineering is applying techniques from neurocognitive science and breaking new grounds in the ways that researchers can model and analyze the cognitive processes of developers as they interact with software artifacts. However, given the novelty of this line of research, only one tool exists to help researchers represent and analyze this kind of multi-modal biometric data. While this tool does help with visualizing temporal eyetracking and physiological data, it does not allow for the mapping of physiological data to source code elements, instead projecting information over images of code. One drawback of this is that researchers are still unable to meaningfully combine and map physiological and eye tracking data to source code artifacts. The use of images also bars the support of long or multiple code files, which prevents researchers from analyzing data from experiments conducted in realistic settings. To address these drawbacks, we propose VITALSE, a tool for the interactive visualization of combined multi-modal biometric data for software engineering tasks. VITALSE provides interactive and customizable temporal heatmaps created with synchronized eyetracking and biometric data. The tool supports analysis on multiple files, user defined annotations for points of interest over source code elements, and high level customizable metric summaries for the provided dataset. VITALSE, a video demonstration, and sample data to demonstrate its capabilities can be found at http://www.vitalse.app. 
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  2. A large portion of the cost of any software lies in the time spent by developers in understanding a program’s source code before any changes can be undertaken. Measuring program comprehension is not a trivial task. In fact, different studies use self-reported and various psycho-physiological measures as proxies. In this research, we propose a methodology using functional Near Infrared Spectroscopy (fNIRS) and eye tracking devices as an objective measure of program comprehension that allows researchers to conduct studies in environments close to real world settings, at identifier level of granularity. We validate our methodology and apply it to study the impact of lexical, structural, and readability issues on developers’ cognitive load during bug localization tasks. Our study involves 25 undergraduate and graduate students and 21 metrics. Results show that the existence of lexical inconsistencies in the source code significantly increases the cognitive load experienced by participants not only on identifiers involved in the inconsistencies but also throughout the entire code snippet. We did not find statistical evidence that structural inconsistencies increase the average cognitive load that participants experience, however, both types of inconsistencies result in lower performance in terms of time and success rate. Finally, we observe that self-reported task difficulty, cognitive load, and fixation duration do not correlate and appear to be measuring different aspects of task difficulty. 
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  3. It has been well documented that a large portion of the cost of any software lies in the time spent by developers in understanding a program’s source code before any changes can be undertaken. One of the main contributors to software comprehension, by subsequent developers or by the authors themselves, has to do with the quality of the lexicon, (i.e., the identifiers and comments) that is used by developers to embed domain concepts and to communicate with their teammates. In fact, previous research shows that there is a positive correlation between the quality of identifiers and the quality of a software project. Results suggest that poor quality lexicon impairs program comprehension and consequently increases the effort that developers must spend to maintain the software. However, we do not yet know or have any empirical evidence, of the relationship between the quality of the lexicon and the cognitive load that developers experience when trying to understand a piece of software. Given the associated costs, there is a critical need to empirically characterize the impact of the quality of the lexicon on developers’ ability to comprehend a program. In this study, we explore the effect of poor source code lexicon and readability on developers’ cognitive load as measured by a cutting-edge and minimally invasive functional brain imaging technique called functional Near Infrared Spectroscopy (fNIRS). Additionally, while developers perform software comprehension tasks, we map cognitive load data to source code identifiers using an eye tracking device. Our results show that the presence of linguistic antipatterns in source code significantly increases the developers’ cognitive load. 
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  4. Traditionally, program comprehension research relies heavily on indirect measures of comprehension, where subjects report on their own comprehension levels or summarize part of an artifact so that researchers can instead deduce the level of comprehension. However, there are several potential issues that can result from using these indirect measures because they are prone to participant biases and implicitly deduce comprehension based on various factors. The proposed research presents a framework to move towards more objective measures of program comprehension through the use of brain imaging and eye tracking technology. We aim to shed light on how the human brain processes comprehension tasks, specifically what aspects of the source code cause measurable increases in the cognitive load of developers in both bug localization tasks, as well as code reviews. We discuss the proposed methodology, preliminary results, and overall contributions of the work. 
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