Learning effective query transformations for enhanced requirements trace retrieval

In automated requirements traceability, significant improvements can be realized through incorporating user feedback into the trace retrieval process. However, existing feedback techniques are designed to improve results for individual queries. In this paper we present a novel technique designed to extend the benefits of user feedback across multiple trace queries. Our approach, named Trace Query Transformation (TQT), utilizes a novel form of Association Rule Mining to learn a set of query transformation rules which are used to improve the efficacy of future trace queries. We evaluate TQT using two different kinds of training sets. The first represents an initial set of queries directly modified by human analysts, while the second represents a set of queries generated by applying a query optimization process based on initial relevance feedback for trace links between a set of source and target documents. Both techniques are evaluated using requirements from theWorldVista Healthcare system, traced against certification requirements for the Commission for Healthcare Information Technology. Results show that the TQT technique returns significant improvements in the quality of generated trace links.