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首页> 外文会议>2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering Education and Training >Linking Code Readability, Structure, and Comprehension Among Novices: It's Complicated
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Linking Code Readability, Structure, and Comprehension Among Novices: It's Complicated

机译：在新手之间链接代码的可读性，结构和理解：非常复杂

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Novices' functionally-correct code is often redundant, verbose, or un-idiomatic. Such code could indicate shallow understanding of the programming language, or unfamiliarity with experts' preferences for code structure. Understanding why novices write poorly is important for designing instruction and tools to help novices write elegantly. 231 novices judged style and readability for sets of code snippets targeting seven topics. Within each set, functionality was the same, but the writing followed either common novice patterns or a more elegant, "expert" pattern. Overall, 76% of novices thought the "expert" snippets had the best style, but only 64% said those snippets were most readable. However, comprehension was similar for both "expert" and novice patterns, regardless of readability preferences. This suggests that students who prefer novice patterns do not necessarily have deep misunderstandings about the programming language. One topic included a code-writing task, and students' readability preferences were predictive of their code-writing patterns, suggesting that readability preferences reflect writing choices rather than comprehension. Thus, novices may benefit from lightweight tools that identify common patterns and suggest an "expert" solution, while helping them see that the "expert" solution is more readable than they think.
机译：新手在功能上正确的代码通常是多余的，冗长的或惯用的。这样的代码可能表明对编程语言的了解不深，或者不熟悉专家对代码结构的偏爱。理解为什么新手写作不好对设计指导和工具以帮助新手优雅写作很重要。 231位新手判断了针对七个主题的代码片段集的样式和可读性。在每个集合中，功能都是相同的，但是编写时遵循的是常见的新手模式或更为优雅的“专家”模式。总体而言，有76％的新手认为“专家”摘要具有最好的样式，但是只有64％的新手认为这些摘要最易读。但是，无论“可读性”偏好如何，“专家”模式和新手模式的理解都是相似的。这表明喜欢新手模式的学生不一定对编程语言有深刻的误解。一个主题包括一个代码编写任务，并且学生的可读性偏好是他们代码编写模式的预测，这表明可读性偏好反映的是写作选择而不是理解能力。因此，新手可能会从轻量级工具中受益，这些工具可以识别常见模式并提出“专家”解决方案，同时帮助他们看到“专家”解决方案比他们想象的更具可读性。

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