CAREER: Understanding and Supporting Programmer Cognition

职业：理解和支持程序员认知

• 批准号: 2045272
• 负责人: Christopher Parnin
• 金额: $ 55.59万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045272&HistoricalAwards=false
• 关键词: CAREER; Understanding; Supporting; Programmer; Cognition

Cognition is central to any programming task—from understanding and reading source code, selecting programming abstractions and algorithms, and problem-solving to debugging implementations. In the software industry, programmers are faced with numerous challenges that stress the limits of human cognition, leading to errors, lost productivity, and ultimately failed projects. For example, programmers face an exploding array of choices in which languages, platforms, and frameworks they choose to learn and build expertise—all of which may become obsolete or irrelevant when switching to a new project or team. This project’s goal is to understand programmer cognition through brain-imaging techniques and low-cost, widely available, high-fidelity biometric sensors. The anticipated result is the design of tools that more effectively support programmers in working with complex code and acquiring expertise. Beyond the general benefit of better-educated programmers, techniques for teaching computer programming are important in particular because programming is a crucial skill for a digitally literate society.Past research on programmer cognition has relied on psychological and observational experiments using indirect techniques, such as comparing task performance or having programmers articulate their thoughts in think-aloud protocols. To overcome these limitations, the project will first establish a methodology for conducting a series of brain-imaging studies to obtain brain-activation contrasts between an experimental comprehension task and a control task. To study code complexity, the project will use a parameterized analysis of code, where code is systematically selected to contrast different complexity metrics. The project will develop techniques for automatically reducing code complexity based on discovered principles. To study programmer expertise, the project will use brain-imaging techniques to identify brain regions associated with expertise, identify any cortical differences, and examine any differences in neural efficiency. Furthermore, the learning trajectories of programmers acquiring a new skill will be used to understand the time course of knowledge acquisition. From these studies, one is able to explain the neural mechanics of cognition in programming and derive more effective mental representations, strategies, and training techniques for rapid training of expertise. More generally, understanding and supporting programmer cognition has broader impacts on reducing frustration and dropout in newcomers and identifying unique support needed for neuro-diverse populations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

认知是任何编程任务的核心从理解和阅读源代码，选择编程抽象和算法，解决问题到调试实现。在软件行业，程序员面临着许多挑战，这些挑战强调人类认知的局限性，导致错误，生产力损失，最终导致项目失败。例如，程序员面临着一系列爆炸性的选择，他们选择学习和建立专业知识的语言，平台和框架-所有这些都可能在切换到新项目或团队时变得过时或无关紧要。该项目的目标是通过脑成像技术和低成本，广泛使用的高保真生物传感器来了解程序员的认知。预期的结果是设计出更有效地支持程序员处理复杂代码和获取专业知识的工具。除了受过良好教育的程序员的普遍好处之外，教授计算机编程的技术也特别重要，因为编程是数字化社会的一项关键技能。过去对程序员认知的研究依赖于使用间接技术的心理和观察实验，例如比较任务表现或让程序员在有声思维协议中表达自己的想法。为了克服这些局限性，该项目将首先建立一种方法来进行一系列的脑成像研究，以获得实验理解任务和控制任务之间的脑激活对比。为了研究代码复杂性，该项目将使用代码的参数化分析，其中系统地选择代码以对比不同的复杂性度量。该项目将开发基于发现的原则自动降低代码复杂性的技术。为了研究程序员的专业知识，该项目将使用脑成像技术来识别与专业知识相关的大脑区域，识别任何皮层差异，并检查神经效率的任何差异。此外，程序员获取新技能的学习轨迹将用于了解知识获取的时间过程。从这些研究中，人们能够解释编程中认知的神经机制，并获得更有效的心理表征，策略和培训技术，以快速培训专业知识。更普遍地说，理解和支持程序员认知对减少新手的挫折和辍学以及确定神经多样性人群所需的独特支持具有更广泛的影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。