Utilizing supporting evidence to improve dynamic requirements traceability

Requirements traceability provides critical support throughout all phases of a software development project. However practice has repeatedly shown the difficulties involved in long term maintenance of traditional traceability matrices. Dynamic retrieval methods minimize the need for creating and maintaining explicit links and can significantly reduce the effort required to perform a manual trace. Unfortunately they suffer from recall and precision problems. This paper introduces three strategies for incorporating supporting information into a probabilistic retrieval algorithm in order to improve the performance of dynamic requirements traceability. The strategies include hierarchical modeling, logical clustering of artifacts, and semi-automated pruning of the probabilistic network. Experimental results indicate that enhancement strategies can be used effectively to improve trace retrieval results thereby increasing the practicality of utilizing dynamic trace retrieval methods.