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

软件渗透到日常活动（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领域概念，并与其他软件工程师进行通信。因此，这些名称和注释的质量，即软件词典，对于理解软件做什么以及如何做是至关重要的。研究人员之前已经发现了一系列导致低质量词汇的做法。推测是，这样的实践可能会损害软件理解，并可能导致误解，最终导致软件错误。然而，没有客观证据支持这种明显的因果关系。因此，当软件开发人员试图理解包含较差词汇的软件时，本项目试图通过测量认知负荷的变化来描述词汇质量对软件理解的影响。本研究将有助于深入了解目前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，最终将积极地影响开发人员的生产力、软件开发和维护的成本，以及软件的质量。本研究的结果也将对STEM教育产生重大的积极影响，因为该项目将使我们能够为学生提供如何编写软件词典的指导，从而最大限度地减少程序理解过程中的认知负荷。这个项目的总体目标是描述词典的质量对程序理解和软件维护的影响。核心假设是，低质量的词典与开发人员在理解源代码时的高认知负荷和糟糕的软件维护相关。为此，我们将：1。确定直接和客观的度量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典的困难/无法相关。确定文献中记录的哪些导致低质量词汇的实践与高认知负荷有关。工作假设是，与其他类型的不良词典相比，某些类型的不良词典（例如词典与源代码功能的不一致）对程序理解的影响要大得多。识别在软件维护任务中妨碍程序理解的低质量词汇的类型。研究者假设，某些类型的不良词汇的存在将显著增加理解一段代码所需的时间，在某些情况下，它将导致执行软件维护任务时的失败。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.