SHF: SMALL: Automatically Supporting Developer Learning in Context

努力创建和发展大型软件系统的软件开发人员发现自己经常需要学习。他们加入了一个新的项目，在那里他们需要学习一个新的软件系统和开发团队-S文化。他们经常需要学习新的应用程序编程接口(API)或现有API的较新组件。该项目解决了对有用的自动化支持的需求，以帮助软件开发人员学习API以及如何有效地使用它们来解决手头的任务。所设想的系统在服务器上执行底层自动分析，该服务器从用户-S上下文和用户允许的当前维护或演进任务中获取信息，并在他们执行任务时发送回学习块。有了更有效的学习方式，软件开发可以更有效率，并提供更可靠、更高质量的软件，这对越来越依赖软件的社会产生了广泛的影响。这个项目将通过解决三个主要挑战来促进最先进的技术，将这种对开发人员学习的自动化支持投入实际应用。首先，我们将开发分析技术来弥合单语句级分析和整个方法级分析之间的差距，以便在多语句、算法步骤级自动提取、描述和概括源代码中的信息。其次，我们将开发工具，自动识别、提取和分类电子邮件、问答论坛和其他开发人员交流等混合文本代码构件中的不同类型的信息，如事实、(正面和负面)意见/建议和使用信息。第三，我们将开发工具来自动识别开发人员的相关上下文，并确定该上下文的相关学习金块。在项目中开发的新的分析方法、结果工具、数据集和实验基础设施将被发布，这将使其他研究人员和实践者能够在项目的基础上再接再厉-S的结果，并最终将促进软件工程领域的知识和理解。

Software developers striving to create and evolve large software systems find themselves frequently in need of learning. They join a new project where they need to learn a new software system and the development team?s culture. They frequently need to learn new Application Programming Interfaces (APIs), or newer components of existing APIs. The project addresses the need for useful automated support for helping software developers learn APIs and how to use them effectively solve the task at hand. The envisioned system performs the underlying automated analysis on a server which takes information from the user?s context and current maintenance or evolution task, as allowed by the user, and sends back learning nuggets as they perform their tasks. With more effective ways to learn, software development could be more efficient and provide more reliable, higher quality software, which has a broad impact on society which depends increasingly on software.This project will contribute to the state of the art by tackling three major challenges to bring this kind of automated support for developer learning into practical use. First, we will develop analysis techniques to bridge the gap between single statement and whole method level analyses for automatic extraction, description, and generalization of information from source code at the multi-statement, algorithm-step level. Second, we will develop tools that automatically identify, extract and categorize different kinds of information such as facts, (positive and negative) opinions/advice, and usage information in mixed text-code artifacts such as emails, question-answer forums, and other developer communications. Third, we will develop tools to automatically identify the relevant context of the developer and identify the relevant learning nuggets for that context. The novel approach to analyses, the resulting tools, data sets, and experimental infrastructure developed within the project will be released, which will enable other researchers and practitioners to build on the project?s results and will ultimately advance knowledge and understanding within the field of Software Engineering.