Assisting code search with automatic Query Reformulation for bug localization

Abstract

Source code retrieval plays an important role in many software engineering tasks. However, designing a query that can accurately retrieve the relevant software artifacts can be challenging for developers as it requires a certain level of knowledge and experience regarding the code base. This paper demonstrates how the difficulty of designing a proper query can be alleviated through automatic Query Reformulation (QR) - an under-the-hood operation for reformulating a user's query with no additional input from the user. The proposed QR framework works by enriching a user's search query with certain specific additional terms drawn from the highest-ranked artifacts retrieved in response to the initial query. The important point here is that these additional terms injected into a query are those that are deemed to be “close” to the original query terms in the source code on the basis of positional proximity. This similarity metric is based on the notion that terms that deal with the same concepts in source code are usually proximal to one another in the same files. We demonstrate the superiority of our QR framework in relation to the QR frameworks well-known in the natural language document retrieval by showing significant improvements in bug localization performance for two large software projects using more than 4,000 queries.