  In most safety-critical domains the need for traceability is prescribed by
certifying bodies. Trace links are generally created among requirements,
design, source code, test cases and other artifacts, however, creating such
links manually is time consuming and error prone. Automated solutions use
information retrieval and machine learning techniques to generate trace links,
however, current techniques fail to understand semantics of the software
artifacts or to integrate domain knowledge into the tracing process and
therefore tend to deliver imprecise and inaccurate results. In this paper, we
present a solution that uses deep learning to incorporate requirements artifact
semantics and domain knowledge into the tracing solution. We propose a tracing
network architecture that utilizes Word Embedding and Recurrent Neural Network
(RNN) models to generate trace links. Word embedding learns word vectors that
represent knowledge of the domain corpus and RNN uses these word vectors to
learn the sentence semantics of requirements artifacts. We trained 360
different configurations of the tracing network using existing trace links in
the Positive Train Control domain and identified the Bidirectional Gated
Recurrent Unit (BI-GRU) as the best model for the tracing task. BI-GRU
significantly out-performed state-of-the-art tracing methods including the
Vector Space Model and Latent Semantic Indexing.
