Multiple visualizations and debugging

Hejmady, Prateek; Narayanan, N. Hari

doi:10.1145/2212776.2223670

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Second,participantbackgroundandexperiencewithopensourcelicensesdon'tseemtoinfluence licensecomprehensionstrategies.Ourqualitativeresultsshowthatopensourcelicenseexpertsand non-expertsarespreadoverthe3groups.Thisisdifferentfrom (Bednarik,etal.,2006a;Hejmady,et al,2012a),asbothindicatedthatprogrammingexpertstendedtofocusontheprogramtext(andits executedoutput).Thismayalsoindicatedifferencesinspecificexpertiseinprogrammingasopposed togeneraldomainexpertise. InthestudyonrequirementscomprehensionbySharafi,etal.…”

Section: Qualitative Descriptionsmentioning

confidence: 99%

Understanding User Engagement With Multi-Representational License Comprehension Interfaces

Avande

Gandhi

Siy

2020

International Journal of Open Source Software and Processes

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License information for any non-trivial open-source software demonstrates the growing complexity of compliance management. Studies have shown that understanding open-source licenses is difficult. Prior research has not examined how developers would use interfaces displaying license text and its graphical models in studying a license. Consequently, a repeatable eye tracking-based methodology was developed to study user engagement when exploring open-source rights and obligations in a multi-modal fashion. Experiences of 10 participants in an exploratory case study design indicate that eye-tracking is feasible to quantitatively and qualitatively observe distinct interaction patterns in the use of license comprehension interfaces. A low correlation was observed between self-reported usability survey data and eye-tracking data. Conversely, a high correlation between eye-tracker and mouse data suggests the use of either in future studies. This paper provides a framework to conduct such studies as an alternative to surveys while offering interesting hypotheses for future studies.

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Section: Qualitative Descriptionsmentioning

confidence: 99%

Understanding User Engagement With Multi-Representational License Comprehension Interfaces

Avande

Gandhi

Siy

2020

International Journal of Open Source Software and Processes

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On Eye Tracking in Software Engineering

Grabinger,

Hauser,

Wolff

et al. 2024

SN COMPUT. SCI.

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Eye tracking is becoming more and more important as a research method within the field of software engineering (SE). Existing meta-analyses focus on the design or conduct of SE eye tracking studies rather than the analysis phase. This article attempts to fill this gap; it presents a systematic literature review of eye tracking studies in the field of SE—focusing mainly on the data analysis methods used. From the IEEE Xplore and ACM digital libraries we gather 125 papers up to the first quarter of 2024. Detailed evaluation provides information on the number of papers that use specific methods of analysis (i.e., descriptive or inferential statistics, and gaze visualization) or settings (e.g., sample size, technical setup, and selected aspects of research design). With the data obtained we can infer the popularity of specific analysis methods in the field. Those results enable efficient work on data analysis tools or education of aspiring researchers and can serve as basis for standardization or guidelines within the community—providing for methods to include as well as current inconsistencies.

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Multiple visualizations and debugging

Cited by 2 publications

References 4 publications

Understanding User Engagement With Multi-Representational License Comprehension Interfaces

Understanding User Engagement With Multi-Representational License Comprehension Interfaces

On Eye Tracking in Software Engineering

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