4 research outputs found
Semantically Enhanced Software Traceability Using Deep Learning Techniques
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.Comment: 2017 IEEE/ACM 39th International Conference on Software Engineering
(ICSE
Information Retrieval based requirement traceability recovery approaches- A systematic literature review
Abstract: The term traceability is an important concept regarding software development. It enables software engineers to trace requirements from their origin to fulfillment. Maintaining traceability manually is a time consuming and expensive job. Information retrieval methods provide a mean of automation for requirement traceability. A visible number of IR based traceability techniques have been proposed in the literature, but the adoption of these techniques in the industry is limited. In this paper, we examine the information retrieval-based traceability recovery approaches through systematic literature review. We presented a synthesis of these techniques. We also identified challenges that are potentially limiting the adoption of IR based traceability recovery approaches. We conclude that term mismatch is a major barrier faced by IR based approaches. We also did classify the approaches that are attempting to solve the term mismatch problem