Automatically capturing source code context of NL-queries for software maintenance and reuse

As software systems continue to grow and evolve, locating code for maintenance and reuse tasks becomes increasingly difficult. Existing static code search techniques using natural language queries provide little support to help developers determine whether search results are relevant, and few recommend alternative words to help developers reformulate poor queries. In this paper, we present a novel approach that automatically extracts natural language phrases from source code identifiers and categorizes the phrases and search results in a hierarchy. Our contextual search approach allows developers to explore the word usage in a piece of software, helping them to quickly identify relevant program elements for investigation or to quickly recognize alternative words for query reformulation. An empirical evaluation of 22 developers reveals that our contextual search approach significantly outperforms the most closely related technique in terms of effort and effectiveness.