An Eye Tracking Study on camelCase and under_score Identifier Styles

Sharif, Bonita; Maletic, Jonathan I.

doi:10.1109/icpc.2010.41

Cited by 110 publications

(60 citation statements)

References 25 publications

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“…For instance, Crosby et al [33] and Bednarik et al [13] used an eye tracker to study how experienced and less experienced developers understand source code. Similarly, Sharif et al [15] relied on eye tracking technology to investigate how different identifier naming conventions influence program comprehension by examining the visual effort spent on identifiers.…”

Section: B Biometrics In Software Engineeringmentioning

confidence: 99%

“…For emotions, researchers have looked at the emotions that developers experience [1], [8], how they might affect productivity [9], [10], and whether one could use interaction logs to predict them [11], [12]. Using biometric sensors, in particular eye-tracking and fMRI, researchers have mainly studied how software developers comprehend code or use tools [13]- [15]. In a previous study, we looked at the use of biometric sensors to assess the difficulty of small code comprehension tasks [16].…”

Section: Introductionmentioning

confidence: 99%

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Stuck and Frustrated or in Flow and Happy: Sensing Developers' Emotions and Progress

Müller

Fritz

2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

123

133

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Software developers working on change tasks commonly experience a broad range of emotions, ranging from happiness all the way to frustration and anger. Research, primarily in psychology, has shown that for certain kinds of tasks, emotions correlate with progress and that biometric measures, such as electro-dermal activity and electroencephalography data, might be used to distinguish between emotions. In our research, we are building on this work and investigate developers' emotions, progress and the use of biometric measures to classify them in the context of software change tasks. We conducted a lab study with 17 participants working on two change tasks each. Participants were wearing three biometric sensors and had to periodically assess their emotions and progress. The results show that the wide range of emotions experienced by developers is correlated with their progress on the change tasks. Our analysis also shows that we can build a classifier to distinguish between positive and negative emotions in 71.36% and between low and high progress in 67.70% of all cases. These results open up opportunities for improving a developer's productivity. For instance, one could use such a classifier for providing recommendations at opportune moments when a developer is stuck and making no progress. Abstract-Software developers working on change tasks commonly experience a broad range of emotions, ranging from happiness all the way to frustration and anger. Research, primarily in psychology, has shown that for certain kinds of tasks, emotions correlate with progress and that biometric measures, such as electro-dermal activity and electroencephalography data, might be used to distinguish between emotions. In our research, we are building on this work and investigate developers' emotions, progress and the use of biometric measures to classify them in the context of software change tasks. We conducted a lab study with 17 participants working on two change tasks each. Participants were wearing three biometric sensors and had to periodically assess their emotions and progress. The results show that the wide range of emotions experienced by developers is correlated with their perceived progress on the change tasks. Our analysis also shows that we can build a classifier to distinguish between positive and negative emotions in 71.36% and between low and high progress in 67.70% of all cases. These results open up opportunities for improving a developer's productivity. For instance, one could use such a classifier for providing recommendations at opportune moments when a developer is stuck and making no progress.

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Section: B Biometrics In Software Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stuck and Frustrated or in Flow and Happy: Sensing Developers' Emotions and Progress

Müller

Fritz

2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

123

133

View full text Add to dashboard Cite

show abstract

“…For example, Sharif et al confirm that scan time and bug detection time are correlated [47]. Eyetracking has been used in studies of identifier style [49,8] and UML diagram layout [46,48], showing reduced comprehension when the code is not in an understandable format.…”

Section: Eye-tracking In Software Engineeringmentioning

confidence: 99%

Improving automated source code summarization via an eye-tracking study of programmers

Rodeghero

McMillan

McBurney

et al. 2014

Proceedings of the 36th International Conference on Software Engineering

165

106

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Source Code Summarization is an emerging technology for automatically generating brief descriptions of code. Current summarization techniques work by selecting a subset of the statements and keywords from the code, and then including information from those statements and keywords in the summary. The quality of the summary depends heavily on the process of selecting the subset: a high-quality selection would contain the same statements and keywords that a programmer would choose. Unfortunately, little evidence exists about the statements and keywords that programmers view as important when they summarize source code. In this paper, we present an eye-tracking study of 10 professional Java programmers in which the programmers read Java methods and wrote English summaries of those methods. We apply the findings to build a novel summarization tool. Then, we evaluate this tool and provide evidence to support the development of source code summarization systems.

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“…One of the first eye-tracking studies in program comprehension was conducted by Crosby et al, who found that experts and novices differ in the way they looked at English and Pascal versions of an algorithm [11]. Since then, several researchers have used eye-tracking to evaluate the impact of developers' eye gaze on comprehension for different kinds of representations and visualizations such as 3D visualizations [37], UML diagrams [51,12], design pattern layout [39], programming languages [44], and identifier styles [40,8]. Researchers have also used eye-tracking to investigate developers' scan patterns for very small code snippets, finding that participants first read the entire code snippet to get an idea of the program [45].…”

Section: Biometric Sensingmentioning

confidence: 99%

“…Studies with sensors for tracking biometric features, such as eye gaze, have generated new insights on developers' work on small code tasks, such as perceptions of difficulty [15], brain activation patterns [42], the scanning patterns of code [36] or the ease of comprehending different representations of code [40,6]. Most of these studies focus on very small code comprehension tasks with a single method or class, in particular, since they require manual linking between the gaze data collected with an eye-tracker and the source code elements a developer looked at.…”

Section: Introductionmentioning

confidence: 99%

Tracing software developers' eyes and interactions for change tasks

Kevic

Walters

Shaffer

et al. 2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

Self Cite

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What are software developers doing during a change task? While an answer to this question opens countless opportunities to support developers in their work, only little is known about developers' detailed navigation behavior for realistic change tasks. Most empirical studies on developers performing change tasks are limited to very small code snippets or are limited by the granularity or the detail of the data collected for the study. In our research, we try to overcome these limitations by combining user interaction monitoring with very fine granular eye-tracking data that is automatically linked to the underlying source code entities in the IDE.In a study with 12 professional and 10 student developers working on three change tasks from an open source system, we used our approach to investigate the detailed navigation of developers for realistic change tasks. The results of our study show, amongst others, that the eye-tracking data does indeed capture different aspects than user interaction data and that developers focus on only small parts of methods that are often related by data flow. We discuss our findings and their implications for better developer tool support.

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An Eye Tracking Study on camelCase and under_score Identifier Styles

Cited by 110 publications

References 25 publications

Stuck and Frustrated or in Flow and Happy: Sensing Developers' Emotions and Progress

Stuck and Frustrated or in Flow and Happy: Sensing Developers' Emotions and Progress

Improving automated source code summarization via an eye-tracking study of programmers

Tracing software developers' eyes and interactions for change tasks

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