Augmenting and structuring user queries to support efficient free-form code search

Abstract

Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code search engine, on top of GitHub and Stack Overflow Q&A data. We evaluate GitSearch in several dimensions to demonstrate that (1) its code search results are correct with respect to user-accepted answers; (2) the results are qualitatively better than those of existing Internet-scale code search engines; (3) our engine is competitive against web search engines, such as Google, in helping users complete solve programming tasks; and (4) GitSearch provides code examples that are acceptable or interesting to the community as answers for Stack Overflow questions.

Automated Query Reformulation for Efficient Search Based on Query Logs From Stack Overflow

“…They further proposed a query reformulation technique that suggests a list of relevant API classes for a natural language query by exploiting keyword-API associations from the questions and answers on Stack Overflow [70]. Similarly, Sirres et al [81] augmented the original query with structural code entities by mining questions and answers from Stack Overflow.…”

Exploring Web Search Engines to Find Architectural Knowledge

“…For example, Wang et al [31] proposed an approach to capture code examples regarding API usage from the web. Sirres et al [32] proposed an approach to improve the effectiveness of source code retrieval by augmenting queries with knowledge from Stackoverflow and Github. Their results improve the effectiveness of source code searching compared to Google.…”

Aroma: code recommendation via structural code search

“…CodeHow [Lv et al 2015] augments the query with API calls which are retrieved from documentation to improve search results. CoCaBu [Sirres et al 2018] augments the query with structural code entities. A developer survey [Sadowski et al 2015] reports the top reason for code search is to find code examples or related APIs, and tools have been created for this need.…”