Improving feature location by transforming the query from natural language into requirements

Software maintenance and evolution activities are responsible for the emergence of a great demand of feature location approaches that search relevant code in a large codebase. However, this search is usually performed manually and relies heavily on developers. In this paper, we propose a feature location approach that, instead of searching directly into code from a natural language query as other approaches do, transforms a natural language query to a query that is made up of the requirements that are located as relevant. Furthermore, our approach limits the scope of the code search space by selecting only the code of those products that hold relevant requirements. We evaluate the overall effectiveness of our approach in the industrial domain of train control software. Our results show that our approach improves in 18.1% the results of precision with regard to searching directly into code, which encourages further research in this direction.