CAREER: On the Foundations of Semantic Code Search

职业：语义代码搜索的基础

基本信息

批准号：
1749936
负责人：
Kathryn Stolee
金额：
$ 50万
依托单位：
North Carolina State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2025-07-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749936&HistoricalAwards=false
关键词：
CAREER Foundations Semantic Code Search

项目摘要

During software development, programmers frequently use search to find code to reuse. At minimum, code reuse requires that the behavior of the found code satisfies the needs of the programmer. Current search tools consume a textual description of the desired code as a query, which ignores the behavior of the source code. Semantic code search finds code based on behavior, and recent research has demonstrated its potential to find source code to reuse code as well as repair software faults. Challenges arise when 1) the desired code does not exist; 2) there are too many results to navigate efficiently; or 3) it is difficult to differentiate between similar code snippets. These challenges are especially pronounced for programmers in languages that are less supported, such as those used by end-user programmers. This research uses an approach to semantic search that leverages a constraint solver as the matching engine. Code fragments are indexed using symbolic analysis to obtain a constraint representation of the code behavior. Given a query in the form of input and output behavior examples, and constraints that represent the code's behavior, the solver determines if the code satisfies the query. This research develops novel techniques to 1) find approximate solutions to semantic queries; 2) enable richer query models; 3) use the constraints to characterize the differences and similarities in behavior between code snippets; and 4) efficiently navigate the space of potential solutions. The broader impact of this research is on the millions of end-user programmers and professional programmers, allowing them to more effectively reuse code.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在软件开发过程中，程序员经常使用搜索来查找代码重复使用。至少，代码重用要求找到的代码的行为满足程序员的需求。当前的搜索工具将所需代码的文本描述作为查询，忽略了源代码的行为。语义代码搜索根据行为找到代码，最近的研究表明，其潜力是找到重用代码和维修软件故障的源代码。当1）不存在所需代码时会出现挑战； 2）有太多的结果有效地导航；或3）很难区分相似的代码段。这些挑战对于使用较少支持的语言的程序员特别明显，例如最终用户程序员使用的挑战。这项研究使用一种方法来利用约束求解器作为匹配引擎。使用符号分析对代码片段进行索引，以获得代码行为的约束表示。给定以输入和输出行为示例的形式进行查询，以及代表代码行为的约束，求解器确定代码是否满足查询。这项研究开发了新技术至1）找到语义查询的近似解决方案； 2）启用更丰富的查询模型； 3）使用约束来表征代码段之间行为的差异和相似性； 4）有效浏览潜在解决方案的空间。这项研究的更广泛影响是对数百万最终用户程序员和专业程序员的影响，使他们能够更有效地重复使用代码。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点评估来支持的，并具有更广泛的影响。

项目成果

期刊论文数量（17）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

An Experience Report on Introducing Explicit Strategies into Testing Checklists for Advanced Beginners

关于将显式策略引入高级初学者测试清单的经验报告

DOI：
10.1145/3587102.3588781
发表时间：
2023
期刊：
Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education
影响因子：
0
作者：
Bai, Gina R.;Sthapit, Sandeep;Heckman, Sarah;Price, Thomas W.;Stolee, Kathryn T.
通讯作者：
Stolee, Kathryn T.

How Graduate Computing Students Search When Using an Unfamiliar Programming Language