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Comparison of visual and textual languages via task modeling

机译：通过任务建模比较视觉和文字语言

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In order for comparative studies of programming languages to be meaningful, differences between the languages need to be carefully studied and well understood. Languages that appear to differ only in syntax (for example, visual vs. textual syntax) may in fact differ greatly in usability. Such differences can confound comparative studies unless they are controlled for. In this paper, we examine the usefulness of fine-grained task modeling for studying the usability of programming languages. We focus on program entry, and demonstrate how to create models of program entry tasks for both visual and textual languages. We also demonstrate how to derive performance time estimates from the models using keystroke-level analysis. A by-product of the model building is a collection of functional-level models that can serve as building blocks for modeling higher-level visual programming tasks. We then report on a comparative study of languages with the same semantics but different syntax (visual and textual). Model-based time predictions of program entry tasks were compared to observed times from an empirical study. The time estimates for the visual condition greatly overestimated the observed times. The primary source of the overestimates appeared to be the time estimate for pointing with the mouse. We then look at three different approaches to improving program entry models. We report on a separate study to calibrate the mouse-pointing time estimate, and demonstrate improved correlation between predicted and observed times with the new estimate. We also apply task modeling to program editing activities, in order to model error recovery behavior during program entry. Finally, we discuss language-specific customization of the keystroke-level operator for mental preparation. We conclude that task modeling is a useful technique for studying differences in the usability of programming languages at the keystroke level.
机译：为了使编程语言的比较研究有意义，需要仔细研究和理解不同语言之间的差异。看起来仅语法不同的语言（例如，视觉语法和文本语法）实际上可能在可用性上有很大差异。除非得到控制，否则这些差异可能会使比较研究混淆。在本文中，我们研究了细粒度任务建模对于研究编程语言的可用性的有用性。我们专注于程序输入，并演示如何为视觉和文本语言创建程序输入任务的模型。我们还演示了如何使用击键级分析从模型得出性能时间估计。模型构建的副产品是功能级别的模型的集合，这些功能级别的模型可以用作对更高级别的可视化编程任务进行建模的构建块。然后，我们报告了对具有相同语义但语法（视觉和文本）不同的语言的比较研究。将程序进入任务的基于模型的时间预测与实证研究中观察到的时间进行比较。视觉条件的时间估计大大高估了观察到的时间。高估的主要来源似乎是用鼠标指向的时间估计。然后，我们研究了三种改进程序输入模型的方法。我们报告了另一项研究，以校准鼠标指向的时间估计，并用新的估计值证明了预测时间和观察时间之间的相关性得到了改善。我们还将任务建模应用于程序编辑活动，以便在程序输入期间对错误恢复行为进行建模。最后，我们讨论了按键准备级别的特定于语言的定制，以进行心理准备。我们得出结论，任务建模是一种有用的技术，可用于在按键级别研究编程语言的可用性差异。

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