Using deep learning to improve the accuracy of requirements to code traceability
Published in Grand Challenges of Traceability: The Next Ten Years, 2017
Recommended citation: Zhao, Yu, Tarannum S. Zaman, Tingting Yu, and Jane Huffman Hayes. "Using Deep Learning to Improve the Accuracy of Requirements to Code Traceability." https://arxiv.org/pdf/1710.03129.pdf#page=22
Motivation: Information retrieval (IR) techniques have been used to recover traceability links between natural language requirements and source code. However, IR techniques are often lack of accuracy. To address this problem, research has shown that mining software repositories and using the mined results combined with the IR techniques can improve the accuracy [1, 4]. For example, Histrace [1] identifies traceability links between requirements and source code through CVS/SVN change logs using a Vector Space Model (VSM). The log messages are tied to changed entities and, thus, can be used to infer traceability links.