SHF: SMALL: Automatically Supporting Developer Learning in Context

SHF：SMALL：自动支持开发人员在上下文中学习

• 批准号: 1422184
• 负责人: Lori Pollock
• 金额: $ 51.57万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422184&HistoricalAwards=false
• 关键词: SHF; SMALL; Automatically; Supporting; Developer

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.

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。他们加入一个新项目，需要学习新的软件系统和开发团队的文化。 他们经常需要学习新的应用程序编程接口 (API) 或现有 API 的更新组件。该项目满足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 以及如何使用它们有效地解决手头的任务。设想的系统在服务器上执行底层自动分析，服务器根据用户的允许从用户的上下文和当前的维护或演化任务中获取信息，并在用户执行任务时发回学习信息。 通过更有效的学习方式，软件开发可以更加高效，并提供更可靠、更高质量的软件，这对日益依赖软件的社会产生广泛影响。该项目将通过解决三大挑战来为最先进的技术做出贡献将这种对开发人员学习的自动化支持投入实际使用。首先，我们将开发分析技术来弥合单语句和整个方法级别分析之间的差距，以便在多语句、算法步骤级别从源代码中自动提取、描述和概括信息。其次，我们将开发自动识别、提取和分类不同类型信息的工具，例如事实、（正面和负面）意见/建议以及混合文本代码工件（例如电子邮件、问答论坛等）中的使用信息。开发者沟通。第三，我们将开发工具来自动识别开发人员的相关背景，并识别该背景的相关学习内容。该项目中开发的新颖的分析方法、由此产生的工具、数据集和实验基础设施将被发布，这将使其他研究人员和从业者能够以该项目的结果为基础，并最终促进该领域的知识和理解。软件工程。