CAREER: Leveraging Objective Measures for Developers' Cognitive Load to Identify and Quantify the Impact of Design, Coding, and Review Practices.

职业：利用开发人员认知负荷的客观测量来识别和量化设计、编码和审查实践的影响。

• 批准号: 1942228
• 负责人: Venera Arnaoudova
• 金额: $ 49.99万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942228&HistoricalAwards=false
• 关键词: CAREER; Leveraging; Objective; Measures; Developers

Software engineers spend the majority of their time reading and understanding software that they or someone else has written. Several catalogs have emerged from academia and industry documenting good and poor practices to make software more flexible, modular, reusable, and understandable. However, those practices are based on experts' opinions, i.e., researchers and software engineers, and not on empirical evidence. Thus, very little is known about the effect of those practices on developers when they read and understand software. With the recent adoption of high-resolution medical imaging technologies in software engineering, the investigator proposes to bring the science behind "best and worst practices" by empirically validating the impact of existing software practices on program comprehension. Moreover, the investigator will identify new practices using scientific methods as opposed to experts' opinions. The proposed work is expected to facilitate new research in the field of software engineering and beyond as it provides a general methodology to objectively measure program comprehension and to empirically evaluate and identify existing and new practices. The outcomes of this research will result in guidelines for students and software engineers on how to write software that minimizes their effort during program comprehension. Once the results have been disseminated and adopted by software engineers, they will be more productive, thus improving their well-being at the workplace. With support from this award, the investigator will integrate the results from this research in her undergraduate and graduate courses where she would teach student how to write software that is easier to understand.The objective of this award is to bring science behind "best" and "worst" software practices by 1) using direct and objective measures to empirically evaluate the impact of existing practices on developers' cognitive load and 2) using those direct and objective measures to empirically identify new practices. In particular, the award targets good and poor software practices (i.e., patterns and antipatterns, respectively) pertaining to the design, code, and reviews of software in the context of bug localization and code review tasks. The central hypothesis is that software development practices impact the cognitive load that developers experience while understanding source code. To test the central hypothesis, mainly a series of controlled experiments will be conducted using a functional near-infrared spectroscopy (fNIRS) and an eyetracking devices. Once the impact of software development practices on program comprehension has been determined, the investigator will recommend guidelines for how to develop and maintain software in a way that 1) it will require less mental effort to be understood by the original developers and by software maintainers; and 2) it will be easier to maintain. The results of the proposed research are also expected to be integrated into automatic recommender tools to improve their recommendations based on empirically validated practices.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.

软件工程师花费他们的大部分时间阅读和理解他们或其他人编写的软件。学术界和工业界已经出现了几个目录，记录了使软件更加灵活、模块化、可重用和可理解的好的和差的实践。然而，这些做法是基于专家的意见，即，研究人员和软件工程师，而不是经验证据。因此，当开发人员阅读和理解软件时，这些实践对他们的影响知之甚少。随着最近在软件工程中采用高分辨率医学成像技术，研究人员建议通过经验验证现有软件实践对程序理解的影响，将“最佳和最差实践”背后的科学带入。此外，调查员将使用科学方法而不是专家意见来确定新的做法。拟议的工作，预计将促进新的研究在软件工程领域和超越，因为它提供了一个通用的方法来客观地衡量程序的理解和经验评估和识别现有的和新的做法。这项研究的结果将导致指导方针，为学生和软件工程师如何编写软件，最大限度地减少他们的努力，在程序的理解。一旦结果被软件工程师传播和采用，他们将更具生产力，从而改善他们在工作场所的福祉。在这个奖项的支持下，研究者将把这项研究的结果整合到她的本科和研究生课程中，她将教学生如何编写更容易理解的软件。该奖项的目标是通过1）使用直接和客观的测量方法来经验性地评估现有实践对开发人员认知负荷的影响，2）使用这些直接和客观的措施，以经验确定新的做法。特别是，该奖项针对好的和差的软件实践（即，模式和反模式），这些模式和反模式分别与缺陷定位和代码评审任务中的软件设计、代码和评审有关。核心假设是软件开发实践影响开发人员在理解源代码时所经历的认知负荷。为了验证中心假设，主要是一系列的控制实验将使用功能近红外光谱（fNIRS）和眼动仪进行。一旦确定了软件开发实践对程序理解的影响，研究者将推荐如何开发和维护软件的指南，以1）原始开发人员和软件维护人员理解软件需要较少的脑力劳动; 2）更容易维护。建议的研究结果也有望被集成到自动推荐工具，以改善他们的建议，根据经验验证的practices.This奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。