嵌入式CLS对眼动追踪复制的影响

Nuris Dwi Setiawan, Bagus Sudirman, Sigit Umar Anggono
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摘要

在软件开发过程中使用不同的编程语言是当前软件开发中常见的一种方法。然而,使用各种语言可能会阻碍开发人员的能力,这一点并不广为人知。本研究模拟了一个计划外的受控研究,检查了在目录编程任务的情况下采用的各种语言。在这项研究中,参与者被分配了用Java和三种类似sql的嵌入式语言之一编写的编程任务。简单的“SQL”凌驾于权威之上,仅限“Java”程序,以及更像Java的混合嵌入式语言。此外,为了记录参与者对在线问题和“任务”解决方案的反应,参与者的眼球运动也被眼动仪记录下来。“眼动仪”或在本研究中称为“Eye- trc”是一种研究软件开发的方法,目前已经发展起来,并提供了关于开发人员如何完成编程任务的更深入的信息。本研究采用Eye-Trc法作为数据收集方法。Eye-Trc数据由31名参与者(大学背景和工业背景)为不同的编程任务获取。为了分析组间变动、专业经验和组内“任务”变量对因变量“完成时间”的影响,本研究采用混合模型方差分析。本研究的结果表明,没有发现对生产力的重要影响,这与最初的研究不同,因为使用的语言。然而,从参与者的编程活动专业知识中发现了同样的效果,表明更有能力的程序员更容易更有效地完成“多语言编程任务”。此外,有人提出,参与者以相同的比例(时间)观看样本代码,以带来“任务”,而不顾技能或语言提供的替代方案。主导阶段的勘探管理在经验或语言选择上也基本保持一致。总的来说,可以得出结论,语言学家的编程阶段没有重要的影响。参与者使用的最高阶段策略是相同的,而不考虑呈现给他们的语言选择。作为对未来研究的建议,应该深入研究多语言编程语言的各种特性的影响,以便得出的结论在各种多语言编程环境中保持正确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IMPACT OF EMBEDDED CLS ON EYE TRACKING REPLICATION
Using different programming languages when software advancement is a familiar method in current software advancement. Nevertheless, using various languages that can hinder developer capacity is not widely known. This research simulated an unplanned controlled study examining the adoption of various languages in the situation of a directory programming task. Participants in this study were given programming tasks written in Java and one of three SQL-like embedded languages.  Simple “SQL” over authority, “Java” program only, and a more Java-like hybrid embedded language. Furthermore, to transcribe the responses to the online quest and the participators' “task” solutions, the participators' eye movements were also recorded with an eye tracker.  “Eye Tracker” or in this study call as “Eye-Trc” is the methodology of the study of software development that has developed nowadays and grants more in-depth info about how developers accomplish programming tasks. This Eye-Trc method is used as a data collection method in this study. Eye-Trc data was get by thirty-one participators (university background and Industrial Background) for different programming tasks. To analyze the impact of inter-group inconstant and professional experience and in-group “task” variables on the dependent variable Time in completion, this study used a mixed model ANOVA. The outcome of this study indicates that an important impact on productivity was not found, this is different from the initial research because of the language used. However, the same effect was found from the participators' expertise in programming activity indicating that more competent programmers were easy to full fill “polyglot programming tasks” more efficiently. In addition, it was raised that participators viewed the specimen code with the same proportion (time) for bringing “task” reckless of skills or language alternative provided. dominant-stage exploration management also remains mostly consistent over the experiences or language alternatives. Overall, it can be concluded that the programming stage of the linguist doesn’t have an important impact. The top-stage strategies that participators used came to be identical reckless of the language alternative presented to them. As a suggestion for future research, the impact of various characteristics of polyglot programming languages should be studied in depth for the conclusions reached to remain correct across various polyglot programming contexts.
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