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

本研究将探讨无缝支持软件开发人员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。 软件开发是一项众所周知的复杂奋进，需要开发人员使用一个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，但它们通常不足以支持开发人员的信息需求。 主要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造成的。 该项目将促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有效和更包容的软件开发人员。它还涉及一个涉及更广泛参与的基本问题，即解决问题的个体差异。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）建立基于他们的认知风格的开发行为的预测模型，（3）创建开发环境，以适应支持开发人员的认知风格，（4）评估预测模型和自适应环境的有效性。这项工作将深入了解认知风格对开发人员如何学习和理解开发历史的影响，以便将他们的工作置于过去和正在进行的变化的背景下。它是新颖的，提供了一个综合的角度对更广泛的历史探索，程序员从事编程时，以及在导致经验验证的预测模型，不仅模型如何开发活动的影响，认知风格的差异，但也由手头的开发任务。 主要目标包括设计和评估一个新的环境，不仅允许开发人员无缝地探索开发历史的不同方面，而且还适应支持开发人员的个人需求。将开发并与科学界共享所收集数据的广泛策划版本，以促进对该主题的进一步研究。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的知识价值进行评估而被认为值得支持影响审查标准。

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.