基于轻量级生物识别传感器的代码理解和专业知识的复制研究
A Replication Study on Code Comprehension and Expertise using Lightweight Biometric Sensors
摘要：
最近，通过使用医学成像，从生理和认知的角度研究了代码理解。Floyd等人（即，最初的研究）使用功能磁共振成像来分类开发人员执行的理解任务的类型，并将这些结果与他们的专业知识联系起来。我们使用参与者（28名计算机科学本科生）在对源代码和自然语言散文执行理解任务时佩戴的轻量级生物识别传感器复制了原始研究。我们开发了机器学习模型来自动识别开发人员正在利用他们的大脑，心脏和皮肤相关信号进行什么样的任务。与原始研究性能相比，仅使用通过单个器械获得的心脏信号实现了最佳改善（BAC 87% vs. 79.1%）。与最初的研究不同，我们无法观察到参与者的专业知识与分类器性能之间的相关性（tau = 0.16，p = 0.31）。我们的研究结果表明，轻量级生物特征传感器可以用来准确地识别理解任务，为研究和实践提供有趣的场景。
英文摘要：
Code comprehension has been recently investigated from physiological and cognitive perspectives through the use of medical imaging. Floyd et al (i.e., the original study) used fMRI to classify the type of comprehension tasks performed by developers and relate such results to their expertise. We replicate the original study using lightweight biometrics sensors which participants (28 undergrads in computer science) wore when performing comprehension tasks on source code and natural language prose. We developed machine learning models to automatically identify what kind of tasks developers are working on leveraging their brain-, heart-, and skin-related signals. The best improvement over the original study performance is achieved using solely the heart signal obtained through a single device (BAC 87% vs. 79.1%). Differently from the original study, we were not able to observe a correlation between the participants' expertise and the classifier performance (tau = 0.16, p = 0.31). Our findings show that lightweight biometric sensors can be used to accurately recognize comprehension tasks opening interesting scenarios for research and practice.
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