CAREER: SHF: Debugging Mental Models

当软件无法按预期运行时，致力于解决此问题的软件开发人员将从事调试活动。调试通常具有挑战性，因为开发人员必须生成关于失败原因的可能假设，通过推理复杂代码的行为来费力地测试假设，并使用调试工具生成和识别相关的运行时状态。对于软件用户来说，这些挑战可能表现为具有未修复缺陷和增加软件成本的软件。此外，专业开发人员拥有的知识如果被提取出来，将有助于更轻松地进行调试。长期以来，专家完成简单编程任务的速度甚至比非专家快十倍以上，很大程度上是因为他们以前见过类似的情况。这个项目解决了调试心理模型的本质问题，以及为理解、外化和共享专家对程序运行时行为的洞察而创建的科学基础。这个项目包含了一套全面的活动，用于对软件开发人员进行研究，并设计新形式的调试工具。将观察到广泛的开发人员样本，以收集从调试任务中捕获心理模型的草图，抽象通用构建块，并计算这些构建块的频率。将设计新类型的调试工具，使开发人员能够通过记录、抽象和重放对程序执行的查询以及可视化程序执行中元素之间的关系来外部化他们的心理模型。新的调试工具将通过用户研究进行评估，以了解开发人员是否以及如何外部化和共享他们的调试心智模型。总之，这些活动将加深对开发人员如何调试的科学理解，并为开发人员更有效地工作创造新的技术。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

When software fails to behave as intended, a software developer working to address this issue engages in the activity of debugging. Debugging is often challenging, as developers must generate possible hypotheses about a failure's cause, laboriously test hypotheses by reasoning about the behavior of complex code, and use debugging tools to generate and identify relevant runtime state. These challenges may be manifest to software users as software with unfixed defects and increased software cost. In addition, expert developers have knowledge which, if extracted, would be valuable in helping to debug more easily. Experts have long been shown to do even simple programming tasks over ten times more quickly than non-experts, largely because they have seen similar situations before. This project addresses the question of the nature of mental models of debugging and the creation of a scientific foundation for understanding, externalizing, and sharing expert insight into the runtime behavior of programs.This project encompasses a comprehensive set of activities for conducting studies of software developers and designing new forms of debugging tools. A broad sample of developers will be observed to collect sketches capturing mental models from debugging tasks, abstract common building blocks, and count the frequency of these building blocks. New types of debugging tools will be designed which enable developers to externalize their mental models by recording, abstracting, and replaying queries over program executions and visualizing relationships between elements within program executions. New debugging tools will be evaluated through user studies to understand if and how developers may externalize and share their debugging mental models. Together, these activities will deepen the scientific understanding of how developers debug and create new techniques for developers to work more effectively.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.