IDE-assisted learning (IDEAL): helping developers to solve their "now problems" now

软件开发人员经常在有效利用新的和不断变化的技术方面遇到问题。当他们在开发任务中需要使用新技术（如库或框架）时，他们通常必须暂停正在做的事情，并从外部来源学习抽象概念和详细的API使用方法。对于从事创造性设计工作的开发人员来说，这种上下文切换可能会大大降低工作效率。对于像Java这样的强类型语言，可以将自动补全之类的工具内建到集成开发环境（IDE）中，以帮助开发人员在很少中断的情况下回忆API细节。对于像Javascript这样的动态类型语言，预测可能的使用场景要困难得多，因为变量的类型信息很难推断。在这项工作中，我们希望利用历史，从现有代码库中挖掘有问题的API以前的使用情况，并通过处理精心策划的API文档来搜索使用模式的提示。使用机器学习、自然语言处理和数据挖掘技术，我们将构建可以集成到IDE中的推荐引擎，为使用动态编程语言、库和框架的开发人员提供帮助。通过这种方式，开发人员将在他们的工作流程中遇到更少的中断，并花费更少的时间阅读API文档，使他们能够更好地专注于手头的开发任务。****

Software developers often have problems in making effective use of new and changing technologies. When they reach a point when the use of a new technology, such as a library or framework, is required in their development task, they often must pause what they are doing, and learn about both the abstract concepts and the detailed API usage from external sources. This context switch can be a significant productivity drain to a developer doing creative design work. With strongly-typed languages such as Java, tools such as autocompletion can be built into the integrated development environment (IDE) to help developers recall API details with little disruption. For dynamically-typed languages such as Javascript, it is much harder to anticipate the likely usage scenario, since the type information of the variables can be hard to infer. In this work, we look to exploit history, by mining both previous usage of the APIs in question from existing code bases, and by processing curated API documentation to search for hints for usage patterns. Using machine learning, natural language processing, and data mining techniques, we will build recommendation engines that can be integrated into the IDE to provide help to developers working with dynamic programming languages, libraries, and frameworks. In this way, developers will encounter fewer interruptions in their work flow, and spend less time reading through API documentation, allowing them to better focus on their development task at hand.****