论文标题
通过任务说明锚定代码可理解性评估
Anchoring Code Understandability Evaluations Through Task Descriptions
论文作者
论文摘要
在代码理解实验中,通常会在开始时告诉参与者哪种代码理解任务需要期望。描述实验场景和实验任务将以有时难以预测和控制的方式影响参与者。特别是,描述甚至提及代码理解任务的难度可能会锚定参与者及其对任务本身的看法。在这项研究中,我们在一项随机,受控的实验中调查了256名参与者(50名软件专业人员和206位计算机科学专业的学生),是否暗示了在任务说明中要理解的代码难度的暗示,以锚定其自身代码的可理解性评分。主观代码评估是对代码理解研究中的开发人员理解的代码的常用措施。因此,重要的是要了解这些措施在诸如锚定效果之类的认知偏见方面有多鲁。我们的结果表明,参与者在评估代码的可理解性评估中受到最初情况描述的影响很大。与未收到提示或易于理解的代码提示的参与者相比,最初的很难理解的代码的提示使参与者评估代码更难理解。这会影响学生和专业人士。我们讨论了可以引起锚定效应的代码理解实验的设计决策和上下文因素的示例,并建议在代码理解研究中使用更强大的理解措施来提高结果的有效性。
In code comprehension experiments, participants are usually told at the beginning what kind of code comprehension task to expect. Describing experiment scenarios and experimental tasks will influence participants in ways that are sometimes hard to predict and control. In particular, describing or even mentioning the difficulty of a code comprehension task might anchor participants and their perception of the task itself. In this study, we investigated in a randomized, controlled experiment with 256 participants (50 software professionals and 206 computer science students) whether a hint about the difficulty of the code to be understood in a task description anchors participants in their own code comprehensibility ratings. Subjective code evaluations are a commonly used measure for how well a developer in a code comprehension study understood code. Accordingly, it is important to understand how robust these measures are to cognitive biases such as the anchoring effect. Our results show that participants are significantly influenced by the initial scenario description in their assessment of code comprehensibility. An initial hint of hard to understand code leads participants to assess the code as harder to understand than participants who received no hint or a hint of easy to understand code. This affects students and professionals alike. We discuss examples of design decisions and contextual factors in the conduct of code comprehension experiments that can induce an anchoring effect, and recommend the use of more robust comprehension measures in code comprehension studies to enhance the validity of results.