Evaluating the specificity of text retrieval queries to support software engineering tasks

Text retrieval approaches have been used to address many software engineering tasks. In most cases, their use involves issuing a textual query to retrieve a set of relevant software artifacts from the system. The performance of all these approaches depends on the quality of the given query (i.e., its ability to describe the information need in such a way that the relevant software artifacts are retrieved during the search). Currently, the only way to tell that a query failed to lead to the expected software artifacts is by investing time and effort in analyzing the search results. In addition, it is often very difficult to ascertain what part of the query leads to poor results. We propose a novel pre-retrieval metric, which reflects the quality of a query by measuring the specificity of its terms. We exemplify the use of the new specificity metric on the task of concept location in source code. A preliminary empirical study shows that our metric is a good effort predictor for text retrieval-based concept location, outperforming existing techniques from the field of natural language document retrieval.