Eye movements in code review

Begel, Andrew; Vrzáková, Hana

doi:10.1145/3216723.3216727

Cited by 30 publications

(22 citation statements)

References 19 publications

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“…They found that on average programmers read from top to bottom 49% of the time. Begel et al [11] report on eye movements during code review and attempt to classify which code elements trigger deliberation and how long reviewers take to verify their hypotheses. They also report on code skimming vs. careful reading.…”

Section: Related Workmentioning

confidence: 99%

Visually Analyzing Students' Gaze on C++ Code Snippets

Peterson

Saddler²,

Blascheck³

et al. 2019

2019 IEEE/ACM 6th International Workshop on Eye Movements in Programming (EMIP)

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The paper presents an eye tracking study with 17 students (12 novices, 5 non-novices) reading C++ methods. The novices were students who participated in the study during the last week of their semester learning C++. The non-novices were senior students who had been exposed to programming before. We report on the reading behavior of three C++ methods that use different language constructs. We analyze fixations at the line level of the code using visualizations to derive insights into code reading. Results show that most transitions were made to code lines that are close to the current line read. We observe that a large percentage of the total fixation duration is made on a small number of lines and that related lines are often viewed together in a series of short fixations.

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Section: Related Workmentioning

confidence: 99%

Visually Analyzing Students' Gaze on C++ Code Snippets

Peterson

Saddler²,

Blascheck³

et al. 2019

2019 IEEE/ACM 6th International Workshop on Eye Movements in Programming (EMIP)

View full text Add to dashboard Cite

show abstract

“…Sharif et al replica ted Uwano et al's study and reported the same results while discussing the impact of expertise. In the same vein, Begel et al [7] performed an eyetracking study with professionals working on 40 code reviews to detect suspicious code elements, while reporting similar findings of code reading visual patterns. Ford et al [27] studied the influence of supplemental technical signals (such as the number of followers, activity, names, or gender) on Pull Request acceptance via an eye tracker.…”

Section: Medical Imaging and Eye-tracking For Sementioning

confidence: 88%

“…However, fMRI does not provide significant evidence about participants' visual interaction with the code itself. We build on previous work and address this problem by capturing participants' attention patterns and interaction via eye-tracking, which has been used to understand developers' visual behavior in code reading [7,87,96] as well as the impact of perceived gender identity in code review [27]. Using eye-tracking in combination with fMRI allows assessing both neural activity and higher-level mental and visual load in human subjects as they complete cognitive tasks.…”

Section: Introductionmentioning

confidence: 99%

Biases and differences in code review using medical imaging and eye-tracking: genders, humans, and machines

Huang

Leach

Sharafi

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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Code review is a critical step in modern software quality assurance, yet it is vulnerable to human biases. Previous studies have clarified the extent of the problem, particularly regarding biases against the authors of code, but no consensus understanding has emerged. Advances in medical imaging are increasingly applied to software engineering, supporting grounded neurobiological explorations of computing activities, including the review, reading, and writing of source code. In this paper, we present the results of a controlled experiment using both medical imaging and also eye tracking to investigate the neurological correlates of biases and differences between genders of humans and machines (e.g., automated program repair tools) in code review. We find that men and women conduct code reviews differently, in ways that are measurable and supported by behavioral, eye-tracking and medical imaging data. We also find biases in how humans review code as a function of its apparent author, when controlling for code quality. In addition to advancing our fundamental understanding of how cognitive biases relate to the code review process, the results may inform subsequent training and tool design to reduce bias. CCS CONCEPTS • Software and its engineering → Collaboration in software development; • Human-centered computing → Empirical studies in collaborative and social computing.

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“…Researchers have used eye-tracking techniques to investigate the viewing strategies of developers while performing code review tasks. These studies include analyzing the gaze patterns of developers in code review [41], understanding the impact of expertise in viewing strategies [37], and detecting suspicious code elements through viewing patterns [3]. They found that a complete scan of the whole code helps students to find defects f and share similar findings on code reading patterns.…”

Section: Related Workmentioning

confidence: 99%

Trustworthiness Perceptions in Code Review

Bertram

Hong

Huang

et al. 2020

Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Background: Automated program repair and other bug-fixing approaches are gaining attention in the software engineering community. Automation shows promise in reducing bug fixing costs. However, many developers express reluctance about accepting machinegenerated patches into their codebases. Aims: To contribute to the scientific understanding and the empirical investigation of human trust and perception with regards to automation in software maintenance. Method: We design and conduct an eye-tracking study investigating how developers perceive trust as a function of code provenance (i.e., author or source). We systematically vary provenance while controlling for patch quality. Results: In our study of ten participants, overall visual code scanning and the distribution of attention differed across identical code patches labeled as human-vs. machine-written. Participants looked more at the source code for human-labeled patches and looked more at tests for machine-labeled patches. Participants judged human-labeled patches to have better readability and coding style. However, participants were more comfortable giving a critical task to an automated program repair tool. Conclusion: We find that there are significant differences in code review behavior based on trust as a function of patch provenance. Further, we find that important differences can be revealed by eye tracking. Our results may inform the subsequent design and analysis of automated repair techniques to increase developers' trust and, consequently, their deployment. CCS CONCEPTS • Software and its engineering → Software creation and management.

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Eye movements in code review

Cited by 30 publications

References 19 publications

Visually Analyzing Students' Gaze on C++ Code Snippets

Visually Analyzing Students' Gaze on C++ Code Snippets

Biases and differences in code review using medical imaging and eye-tracking: genders, humans, and machines

Trustworthiness Perceptions in Code Review

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