Toward Reference Models for Requirements Traceability
IEEE Transactions on Software Engineering
Modern Information Retrieval
Recovering Traceability Links between Code and Documentation
IEEE Transactions on Software Engineering
Towards Method-Driven Trace Capture
CAiSE '97 Proceedings of the 9th International Conference on Advanced Information Systems Engineering
Advancing Candidate Link Generation for Requirements Tracing: The Study of Methods
IEEE Transactions on Software Engineering
Incremental Approach and User Feedbacks: a Silver Bullet for Traceability Recovery
ICSM '06 Proceedings of the 22nd IEEE International Conference on Software Maintenance
Introduction to Information Retrieval
Introduction to Information Retrieval
A machine learning approach for tracing regulatory codes to product specific requirements
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Automated Requirements Traceability: The Study of Human Analysts
RE '10 Proceedings of the 2010 18th IEEE International Requirements Engineering Conference
Toward actionable, broadly accessible contests in software engineering
Proceedings of the 34th International Conference on Software Engineering
Proceedings of the 34th International Conference on Software Engineering
Improving trace accuracy through data-driven configuration and composition of tracing features
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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In automated requirements trace retrieval, significant improvements can be realized through incorporating user feedback. In this paper we introduce a relatively new technique named Direct Query Manipulation (DQM) and compare its effectiveness against Rocchio, the current defacto standard for integrating user feedback into automated tracing methods. The two techniques are evaluated empirically through a series of simulations and a user study, conducted by tracing requirements for WorldVista, an electronic healthcare information system against requirements from the Certification Commission for Healthcare Information Technology. Our results show that both Rocchio and DQM return significant improvements in trace quality in comparison to the vector space model, a fully automated technique. DQM performs slightly better than Rocchio in terms of trace quality with minimal difference in human effort. The hybrid approach provides further improvement over both individual approaches of DQM and Rocchio.