Title: Empirical Study of a Probabilistic Network Model for Retrieving Traceability Links
Published: March 2005
Authors: Xuchang Zou, Raffaella Settimi, Jane Cleland-Huang, and Chuan Duan
Abstract: Requirements traceability provides critical support in helping manage software system evolvement. Establishing and maintaining trace links are often arduous problems which require intensive human effort, if traces need to be evaluated manually. Automatic retrieval tools can help maintain traceability links by dynamically identifying traces between artifacts. In order to effectively reduce the effort involved in manual links discrimination, such automatic tools must achieve high retrieval performance. This paper presents results of experimental studies to analyze the performance of a dynamic trace retrieval approach implementing a probabilistic information retrieval network. An implementation of the retrieval approach described in the paper involves the definition of a small training set for which traces must be known. This can be accomplished by either using past knowledge or by manually evaluating the traces in the training set. A study explores the effect of different size training sets on the retrieval performance of the automatic tool. A second study analyzes methods for defining confidence values, which are attached to each (un)retrieved trace to indicate how confident we are that the dynamically retrieved trace represents a true link, or vice versa that the not retrieved link is a false link. The results of this research are beneficial for enhancing the utility and performance of dynamic tracing tools.
Full Paper: [pdf]