CRII: SHF: Quantifying the Impact of Poor Quality Lexicon on Developers' Cognitive Load.

软件渗透到日常活动中（例如，计算机、电话、游戏）以及安全关键行业（例如，交通、医疗、电力）：软件无处不在。当软件工程师构建软件时，他们使用变量名和注释来嵌入域概念并与其他软件工程师进行通信。因此，这些名称和评论的质量，即，软件词典，对于理解软件做什么和如何做至关重要。2研究人员以前已经确定了一系列导致质量差的词典的实践。推测是，这样的做法可能会损害软件的理解，并可能导致误解，最终软件错误。然而，没有客观证据支持这种明显的因果关系。因此，本项目旨在通过测量软件开发人员试图理解包含不良词汇的软件时认知负荷的变化来表征词汇质量对软件理解的影响。该研究将有助于深入理解迄今为止推测的软件词汇对程序理解过程中认知负荷的影响。更好地理解糟糕的词汇如何影响软件理解，最终将对开发人员的生产力、软件开发和维护的成本以及软件质量产生积极影响。这项研究的成果也将有一个显着的STEM教育的积极影响，因为该项目将使我们能够为学生提供指导方针，如何编写软件词典，最大限度地减少程序理解过程中的认知负荷。该项目的总体目标是表征程序理解和软件维护的词典质量的影响。中心假设是，一个低质量的词汇与高认知负荷的开发人员，而理解源代码和软件维护差。为此，我们将：1.确定直接和客观的措施来量化词汇质量对开发人员认知负荷的影响。这里的工作假设是，已知与认知负荷相关的生理测量将与自我报告的理解软件词汇的困难/能力相关。找出文献中记载的导致低质量词汇的实践与高认知负荷有关。工作假设是，某些类型的不良词汇，如与源代码功能的词汇不一致，将有一个显着更高的影响程序理解相比，其他类型的不良词汇。识别在软件维护任务中阻碍程序理解的低质量词典类型。研究人员假设，某些类型的不良词汇的存在将显着增加所需的时间来理解一段代码，在某些情况下，它会导致失败，而执行软件维护任务。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，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。

Software pervades everyday activities (e.g., computers, phones, games) as well as safety critical industries (e.g., transportation, medical, power): software is everywhere. When software engineers build software they use variable names and comments to embed domain concepts and to communicate with other software engineers. Thus, the quality of those names and comments, i.e., the software lexicon, is of paramount importance for understanding what the software does and how it does it. Researchers have previously identified a set of practices that lead to poor quality lexicon. The speculation is that such practices will possibly impair software understanding and might cause misunderstandings and eventually software bugs. However, there is no objective evidence to support this apparent causal relationship. Thus, this project seeks to characterize the impact of the quality of the lexicon on software understanding by measuring the change of cognitive load when software developers are trying to understand software that contains poor lexicon. The proposed research is expected to contribute in-depth understanding of the so far speculated impact of software lexicon on cognitive workload during program comprehension. A better understanding of how poor lexicon can affect software understanding will, ultimately, positively impact developers' productivity, the cost of software development and maintenance, and the quality of the software. The outcomes of this research will also have a significant positive impact on STEM education as the project will allow us to provide guidelines for students how to write software lexicon that minimizes the cognitive load during program comprehension.The overall objective of this project is to characterize the impact of the quality of the lexicon on program comprehension and on software maintenance. The central hypothesis is that a low-quality lexicon correlates both with high cognitive load of developers while understanding source code and with poor software maintenance. To this end we will:1. Identify direct and objective measures to quantify the impact of lexicon quality on developers' cognitive load. The working hypothesis here is that physiological measures, known to relate to cognitive load, will correlate with self-reported difficulty/inability to understand the software lexicon.2. Identify which of the practices that are documented in the literature to lead to low-quality lexicon are associated with high cognitive load. The working hypothesis is that certain types of poor lexicon, such as the inconsistency of the lexicon with the source code functionality, will have a significantly higher impact on program comprehension compared to other types of poor lexicon.3. Identify types of poor quality lexicon that hinder program comprehension during software maintenance tasks. The investigator hypothesizes that the presence of certain types of poor lexicon will significantly increase the time needed to understand a piece of code and in some cases, it will lead to failure while performing software maintenance tasks.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.