SHF: Small: Solving the Search for Relevant Code in Large Repositories with Lightweight Specifications

SHF：小：用轻量级规范解决大型存储库中相关代码的搜索

• 批准号: 1218265
• 负责人: Sebastian Elbaum
• 金额: $ 44.9万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218265&HistoricalAwards=false
• 关键词: SHF; Small; Solving; Search; Relevant

Searching for relevant code is a common task among programmers, one that is becoming more crucial to their productivity as open code repositories grow in size and programmers have the opportunity to reuse the resources therein. Yet, the code search tools for programmers to leverage such massive repositories have barely evolved in the last decade. Programmers still search by specifying keywords, an approach that may perform very inconsistently as it depends on the programmer's ability to provide terms that match how the programs in the repository are implemented or documented. This is problematic because a slightly wrong keyword could miss relevant results and return mostly spurious results, reducing programmer's productivity.This research aims to address these limitations by exploring a code search that is novel in two aspects. First, it allows programmers to search for code by specifying desired code behavior in the form of inputs and outputs. This removes the need for the programmer to imagine how the solution to a problem may have been implemented, and instead the programmer can concentrate on defining what the software should do. Second, given the inputs and outputs describing the desired behavior and the set of programs in a code repository, both automatically encoded as constraints, the proposed approach employs a constraints solver to identify what programs in the repository could satisfy the programmers' specifications, effectively solving the search. This guarantees the search results behave as specified. Developing this approach requires tackling several fundamental challenges including the definition of mappings to automatically and efficiently encode programs as constraints so that solvers can find suitable solutions. Systematic strategies must also be developed to refine the programmers' specifications when they are too weak and return too many matches, and to relax the constraints in order to find partial matches when exact ones are not available. Techniques to support the composition of partial matches will also be necessary to scale the approach to larger and more diverse search queries. Last, infrastructure must be built and studies conducted to determine whether the proposed approach can be cost-effective in practice. Use of these techniques will change the way programmers operate, directly impacting their productivity by enabling them to truly leverage existing code in increasingly rich code repositories.

搜索相关代码是程序员中的一项常见任务，随着开放代码存储库的规模不断增长，该任务对其生产力变得越来越重要，并且程序员有机会重复使用其中的资源。然而，在过去的十年中，程序员为了利用此类大量存储库的代码搜索工具几乎没有发展。程序员仍然通过指定关键字来搜索，这种方法可能会非常不一致，因为它取决于程序员提供的术语能力，该术语与存储库中的程序的实现或记录方式相匹配。这是有问题的，因为一个稍有错误的关键字可能会错过相关结果并大部分返回伪造的结果，从而降低了程序员的生产力。本研究旨在通过探索两个方面新颖的代码搜索来解决这些局限性。首先，它允许程序员以输入和输出的形式指定所需的代码行为来搜索代码。这消除了程序员的需求，可以想象如何实现问题的解决方案，而程序员可以专注于定义软件应该做什么。其次，给定描述代码存储库中描述所需行为和程序集的输入和输出，两者都自动编码为约束，拟议的方法采用约束求解器来识别存储库中的哪些程序可以满足程序员的规格，从而有效地解决了搜索。这样可以保证搜索结果按规定行为。开发这种方法需要应对几个基本挑战，包括映射的定义以自动有效地将程序编码为约束，以便求解器可以找到合适的解决方案。还必须制定系统的策略，以优化程序员的规格，当时它们的规格太弱并返回太多匹配，并放松约束，以便在没有确切的比赛时找到部分匹配项。支持部分匹配的组成的技术对于扩展大型和更多样化的搜索查询的方法也是必要的。最后，必须建立基础设施并进行研究，以确定所提出的方法在实践中是否可以具有成本效益。 这些技术的使用将改变程序员操作的方式，直接通过使他们能够在越来越丰富的代码存储库中真正利用现有代码来影响其生产率。