基于CodeRank和文档结构的概念定位查询重构算法
Improved Query Reformulation for Concept Location using CodeRank and Document Structures
摘要：
在软件维护过程中，开发人员通常要处理大量的软件变更请求。作为其中的一部分，他们通常从请求文本中制定初始查询，然后尝试将请求中讨论的概念映射到软件系统中的相关源代码位置（也称为，概念位置）。不幸的是，研究表明，他们在为改变任务选择正确的搜索词时往往表现不佳。在本文中，我们提出了一种新的技术-ACER-，采取一个初始查询，确定适当的搜索条件，从源代码使用一个新的长期的权重-CodeRank，然后建议有效的改造，利用源文件结构，查询质量分析和机器学习的初始查询。对来自8个主题系统的1，675个基线查询的实验报告称，我们的技术可以改善71%的基线查询，这是非常有前途的。与五个密切相关的现有技术在查询重构的比较，不仅验证了我们的实证研究结果，但也表明我们的技术的优越性。
英文摘要：
During software maintenance, developers usually deal with a significant number of software change requests. As a part of this, they often formulate an initial query from the request texts, and then attempt to map the concepts discussed in the request to relevant source code locations in the software system (a.k.a., concept location). Unfortunately, studies suggest that they often perform poorly in choosing the right search terms for a change task. In this paper, we propose a novel technique --ACER-- that takes an initial query, identifies appropriate search terms from the source code using a novel term weight --CodeRank, and then suggests effective reformulation to the initial query by exploiting the source document structures, query quality analysis and machine learning. Experiments with 1,675 baseline queries from eight subject systems report that our technique can improve 71% of the baseline queries which is highly promising. Comparison with five closely related existing techniques in query reformulation not only validates our empirical findings but also demonstrates the superiority of our technique.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
