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CRII: SHF: Building Visibility into the Cognitive Processes of Software Engineers via Biosensors

CRII：SHF：通过生物传感器构建软件工程师认知过程的可见性

基本信息

批准号：
1755762
负责人：
Christopher Parnin
金额：
$ 15.97万
依托单位：
North Carolina State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-02-01 至 2020-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755762&HistoricalAwards=false
关键词：
CRII SHF Building Visibility into

项目摘要

The goal of this research project is to study cognitive effort associated with tasks that computer programmers perform. This is important because the limits of cognitive load present a barrier to improving a programmer's productivity and quality. Using newly available biometric technologies such as fMRI, EMG and eye-tracking, it is possible to conduct studies of cognitive processes in programmers. Such studies make it possible to provide scientific answers to questions such as which programming language constructs are easier to use, what kinds of training produce better programmers, or how different programming environments and situational variables are more conducive to writing the best code. The main thrust of this project is to overcome the significant challenges that currently prevent researchers in the software engineering community from widely adopting these methods. This project performs some of the groundbreaking work that is needed to provide robust measurements of cognitive processes in programmers using the biometric technologies. The resulting tailored methodologies and measurement devices will support a community of researchers working in this space. The project will develop techniques to use fMRI to measure difficulty in understanding certain programming constructs. This will be done by measuring cognitive effort, as measured by neural efficiency, and level of concentration via neural deactivation. Similarly, measurements of cognitive processes activated during other code comprehension tasks will be obtained. The objective is to obtain a validated set of measures that predict the cognitive load associated with understanding code, which is necessary to create the scientific basis for the studies. The work will lead to combining different measurements, such as eye-tracking and EMG, to establish more sensitive measurement tools. The project will address technical concerns such as maintaining clean signal data from sensors and learning how to calibrate devices to maintain validity of results, as well as practical concerns such as how to perform measurements on human subjects without interfering with the measurements. Ultimately, such studies should lead to a better understanding what constitutes best practices in software development and training of software engineers.

这个研究项目的目标是研究与计算机程序员执行的任务相关的认知努力。这一点很重要，因为认知负荷的限制是提高程序员生产力和质量的障碍。使用最新的生物识别技术，如功能磁共振成像，肌电图和眼动跟踪，有可能在程序员的认知过程进行研究。这些研究可以为以下问题提供科学的答案，例如哪种编程语言结构更容易使用，什么样的培训可以培养出更好的程序员，或者不同的编程环境和情境变量如何更有利于编写最好的代码。这个项目的主旨是克服目前阻止软件工程社区的研究人员广泛采用这些方法的重大挑战。该项目执行了一些开创性的工作，需要提供强大的测量程序员使用生物识别技术的认知过程。由此产生的量身定制的方法和测量设备将支持在这一领域工作的研究人员社区。该项目将开发技术，使用功能磁共振成像来衡量理解某些编程结构的难度。这将通过测量认知努力来完成，如通过神经效率和经由神经失活的集中水平来测量。类似地，将获得在其他代码理解任务期间激活的认知过程的测量。我们的目标是获得一套有效的措施，预测与理解代码相关的认知负荷，这是必要的，为研究创造科学基础。这项工作将导致结合不同的测量方法，如眼动跟踪和EMG，以建立更灵敏的测量工具。该项目将解决技术问题，如保持传感器的干净信号数据，学习如何校准设备以保持结果的有效性，以及实际问题，如如何在不干扰测量的情况下对人类受试者进行测量。最终，这样的研究应该导致更好地理解什么是软件开发和软件工程师培训的最佳实践。