Towards mining replacement queries for hard-to-retrieve traces

Abstract

Automated trace retrieval methods can significantly reduce the cost and effort needed to create and maintain requirements traces. However, the set of generated traces is generally quite imprecise and must be manually evaluated by analysts. In applied settings when the retrieval algorithm is unable to find the relevant links for a given query, a human user can improve the trace results by manually adding additional search terms and filtering out unhelpful ones. However, the effectiveness of this approach is largely dependent upon the knowledge of the user. In this paper we present an automated technique for replacing the original query with a new set of query terms. These query terms are learned through seeding a web-based search with the original query and then processing the results to identify a set of domain-specific terms. The query-mining algorithm was evaluated and fine-tuned using security regulations from the USA government's Health Insurance Privacy and Portability Act (HIPAA) traced against ten healthcare related requirements specifications.