EAGER: Automatic Classification of Programming Difficulties by Mining Programming Events

今天，当一名学生或工业程序员在分配给他/她的任务中遇到困难时，这一事件往往没有被记录下来，也没有被其他人注意到。因此，不可能使用机制来改善难度的效果。在这个项目中，研究人员将通过挖掘程序员与计算机的交互来自动检测编程困难并对其进行分类，从而解决这个问题。具体地说，他们将调查(A)是否可能自动识别造成困难的障碍，以及(B)是否可能确定困难的严重程度。该项目将开启一个新的研究领域，探索如何设计、实施、评估和应用困难检测机制。广泛的影响：如果成功，这项研究将导致未来各种困难缓解机制的工作，包括：(A)允许产业工人和教师同时向面临困难的开发人员推送帮助；(B)通知面临困难的开发人员其他克服类似困难的人所采取的行动，以便他们可以采取类似的行动；(C)允许任务实施者预测他们将遇到的困难类型，从而为任务做好更好的准备；以及(D)使作业定义者了解作业的内在难度，这可导致重新定义或更好地解释作业。这些改进机制可以大大降低与软件开发和高质量教学相关的高成本，并改变协作式软件工程和教育。这种机制可以显著提高工业生产效率，尤其是在分布式软件开发中。教育环境提供了更有说服力的动机，因为学生的害羞和/或缺乏指导时间阻碍了学生困难的及时解决。在计算机科学中，这尤其是一个问题，因为一个小错误可能会被证明是非常昂贵的。难度改善机制将减少这一问题，从而吸引更多种类的学生学习计算机科学，并增强那些已经致力于此的人的能力。

Today, when a student or industrial programmer faces difficulty in some task assigned to him/her, this event often goes unrecorded and unobserved by others. As a result, it is not possible to use mechanisms to ameliorate the effect of the difficulty. In this project, the researchers will address this problem by automatically detecting and classifying programming difficulties by mining programmers' interaction with the computer. Specifically, they will investigate (a) whether it is possible to automatically identify the barrier causing a difficulty and (b) whether it is possible to determine the severity of the difficulty. The project will start a new area of research exploring how difficulty-detection mechanisms should be designed, implemented, evaluated, and applied.Broader impacts: If successful this research will lead to future work on a variety of difficulty amelioration mechanisms, including (a) allowing industrial workers and teachers to synchronously push help to developers facing difficulties; (b) informing developers facing difficulties about actions taken by others who overcame similar difficulties, so that they can take similar actions; (c) allowing assignment doers to anticipate the kind of difficulties they will encounter and thus be better prepared for the assignment; and (d) giving assignment definers an understanding of the inherent difficulty level of the assignment, which can lead to redefinition or better explanation of the assignment. These amelioration mechanisms can substantially reduce the high costs associated with software development and quality teaching, and transform collaborative software engineering and education. Such mechanisms can lead to significant productivity gains in industry, especially in distributed software development. An educational setting provides an even more compelling motivation because shyness of students and/or lack of instructor time prevents student difficulties from being addressed in a timely manner. In computer science this is particularly a problem as a small mistake can prove to be very costly. The difficulty amelioration mechanisms will reduce this problem and thus attract a larger variety of students to computer science and empower those who are already committed to it.