Using Structured Queries for Source Code Search

Software maintenance tasks such as feature location and traceability link recovery are search-oriented. Most of the recently proposed approaches for automation of search-oriented tasks are based on a traditional text retrieval (TR) model in which documents are unstructured representations of text and queries consist only of keywords. Because source code has structure, approaches based on a structured retrieval model may yield improved performance. Indeed, Saha et al. Recently proposed a feature location technique based on structured retrieval that offers improved performance relative to a technique based on traditional TR. Although they use abstract syntax tree (AST) information to structure documents, they nonetheless use content-only (keyword) queries to retrieve documents. In this paper we propose an approach to source code search using AST information to structure queries in addition to documents. Such queries, known as content and structure (CAS) queries, allow developers to search for source code entities based not only on content relevance, but also on structural similarity. After introducing the structured retrieval model, we provide examples that illustrate the trade-off between the simplicity of content-only queries and the power of CAS queries.