CHS: SMALL: Collaborative Research: Adaptive Development Environments: Modeling and Supporting Cognitive Styles of Software Developers

本研究将探讨无缝地支持软件开发人员的不同认知风格和信息需求的机制，以便开发环境可以模拟开发人员的行为并适应他们的个人需求。众所周知，软件开发是一项复杂的工作，它要求开发人员使用信息分散在不同工具和存储库中的环境。尽管集成开发环境可能有所帮助，但它们在支持开发人员的信息需求方面往往是不够的。主要的障碍是由于开发环境不适应开发人员的认知风格和信息需求。这个项目将推进对软件开发人员行为的了解，并将软件开发工具转变为更高效、更包容的软件开发人员群体。它还涉及一个涉及更广泛参与的基本问题，即解决问题的个人差异。适应这些个体差异的模型和工具可能有助于公平竞争。该方法包括四个阶段：(1)进行实证研究以了解开发人员的信息需求和认知风格；(2)基于开发人员的认知风格建立开发人员行为的预测模型；(3)创建适应支持开发人员认知风格的开发环境；(4)评估预测模型和适应环境的有效性。这项工作将产生对认知风格对开发人员如何学习和理解开发历史的影响的深刻理解，从而将他们的工作置于过去和正在进行的变化的背景中。它的新颖之处在于提供了一个更广泛的历史探索集的综合视角，程序员在编程时参与其中，并导致了经验验证的预测模型，该模型不仅模拟了开发活动如何受到认知风格差异的影响，而且还受到手头的开发任务的影响。关键目标包括设计和评估一个新环境，该环境不仅允许开发人员无缝地探索开发历史的不同方面，而且还可以适应以支持开发人员的个人需求。将开发收集数据的广泛整理版本，并与科学界共享，以促进对该主题的进一步研究。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。

This research will investigate mechanisms to seamlessly support the different cognitive styles and information needs of software developers, such that development environments can model the developers' behavior and adapt to their individual needs. Software development is a notoriously complex endeavor that requires developers to use an environment where information is fragmented across different tools and repositories. Although integrated development environments may help, they are often inadequate in supporting developers' information needs. Major barriers are caused by development environments not adapting to the developers' cognitive styles and information needs. This project will advance the knowledge of software developers' behavior and transform software development tools to be more efficient and more inclusive to a wider population of software developers. It also addresses a fundamental issue concerning broader participation, that of individual differences in problem solving. Models and tools that accommodate these individual differences may help level the playing field. The methodology involves four stages: (1) conduct empirical studies to understand developers' information needs and cognitive styles, (2) build predictive models of developer behavior based on their cognitive styles, (3) create development environments that adapt to support developers' cognitive styles, and (4) evaluate the effectiveness of the predictive models and the adaptive environments. This work will generate a deep understanding of the impact of cognitive styles on how developers learn and make sense of the development history to place their work in the context of past and ongoing changes. It is novel in providing an integrated perspective on the broader set of history explorations in which programmers engage when they are programming as well as in leading to empirically validated predictive models that not only model how development activities are influenced by differences in cognitive styles, but also by the development task at hand. Key goals include the design and evaluation of a novel environment that not only allows developers to seamlessly explore different aspects of the development histories, but also adapts to support the individual needs of a developer. Extensive curated versions of the collected data will be developed and shared with the scientific community to promote additional research into the topic.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.