III: Small: Collaborative Research: Creating and Evolving Software via Searching, Selecting and Synthesizing Relevant Source Code

III：小：协作研究：通过搜索、选择和综合相关源代码来创建和发展软件

• 批准号: 0916139
• 负责人: Mark Grechanik
• 金额: $ 15万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916139&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Creating

Software developers rely on reusing source code snippets from existing libraries or applications to develop software features on time and within budget. The reality is such that most previously implemented features are embedded in billions of lines of scattered source code. State-of-the-art code search engines provide no guarantee that retrieved code snippets implement these features. Even if relevant code fragments are located, developers face rather complex task of selecting and moving these fragments into their applications. Finally, synthesizing new functionality by composing selected code fragments requires sophisticated reasoning about the behavior of these fragments and the consequent code. The result of this process is an overwhelming complexity, a steep learning curve, and a significant cost of building customized software.This research program proposes an integrated model for addressing fundamental problems of searching, selecting, and synthesizing (S3) source code. The S3 model relies on integrating program analysis and information retrieval to produce transformative models to automatically search, select, and synthesize relevant source code fragments. The S3 model will directly support new methodologies for software change and automated tools that assist programmers with various development, reuse and maintenance activities. Among the broader impacts the project includes collaboration with industry to transfer technology.

软件开发人员依赖于重用来自现有库或应用程序的源代码片段来按时并在预算范围内开发软件功能。现实情况是，大多数以前实现的特性都嵌入在数十亿行分散的源代码中。最先进的代码搜索引擎不能保证检索到的代码片段实现这些特性。即使找到了相关的代码片段，开发人员也面临着选择并将这些片段移动到应用程序中的相当复杂的任务。最后，通过组合选定的代码片段来合成新功能需要对这些片段的行为和随后的代码进行复杂的推理。这个过程的结果是压倒性的复杂性，陡峭的学习曲线，以及构建定制软件的巨大成本。本研究计划提出一个集成模型，用于解决搜索、选择和合成（S3）源代码的基本问题。S3模型依赖于集成程序分析和信息检索来生成转换模型，以自动搜索、选择和合成相关的源代码片段。S3模型将直接支持软件变更的新方法和自动化工具，帮助程序员进行各种开发、重用和维护活动。在更广泛的影响中，该项目包括与工业界合作转让技术。