EAGER: Automatic Classification of Programming Difficulties by Mining Programming Events

EAGER：通过挖掘编程事件自动分类编程难度

• 批准号: 1250702
• 负责人: Prasun Dewan
• 金额: $ 10万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1250702&HistoricalAwards=false
• 关键词: EAGER; Automatic; Classification; Programming; Difficulties

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.

今天，当一个学生或工业程序员在分配给他/她的一些任务中遇到困难时，这个事件通常不会被其他人记录和观察到。因此，不可能使用机制来改善困难的影响。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和分类编程困难，从而解决这个问题。 具体而言，他们将调查（a）是否可以自动识别导致困难的障碍，以及（B）是否可以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。更广泛的影响：如果成功，该研究将导致未来各种困难改善机制的工作，包括（a）允许产业工人和教师同步向面临困难的开发人员推送帮助;（B）告知面对困难的发展商，其他克服类似困难的发展商所采取的行动，以便他们采取类似行动;（c）让转让人预见他们会遇到的困难，从而为转让作更充分的准备;以及（d）让作业定义者了解作业的内在难度，这可以导致对作业的重新定义或更好的解释。这些改进机制可以大大降低与软件开发和质量教学相关的高成本，并改变协作软件工程和教育。这样的机制可以在工业中，特别是在分布式软件开发中，带来显著的生产率提高。教育环境提供了一个更引人注目的动机，因为学生的害羞和/或缺乏教师的时间阻止学生的困难得到及时解决。 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个问题，从而吸引更多的学生到计算机科学和授权那些谁已经致力于它。