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

基本信息

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

项目摘要

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.

软件开发人员依靠现有库或应用程序重复源代码段来按时和预算范围内开发软件功能。现实是，大多数先前实现的功能都嵌入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代码片段实现这些功能。即使找到了相关的代码片段，开发人员也会面临相当复杂的任务，将这些片段选择和将这些片段移动到其应用程序中。最后，通过编写选定的代码片段来综合新功能需要关于这些片段的行为和随之而来的代码的复杂推理。这个过程的结果是一个压倒性的复杂性，陡峭的学习曲线以及构建定制软件的巨大成本。本研究计划提出了一个集成模型，用于解决搜索，选择和合成的基本问题（S3）源代码。 S3模型依靠集成程序分析和信息检索来生成变革性模型，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更改和自动化工具的新方法，以帮助程序员进行各种开发，再利用和维护活动。在更广泛的影响中，该项目包括与行业合作转移技术。