DF4RT: Deep Forest for Requirements Traceability Recovery Between Use Cases and Source Code
Author:
Affiliation:
1. School of Computer Science and Artificial Intelligence, Wuhan Textile University Engineering Research Center of Hubei Province for Clothing Information, Wuhan Textile University,Wuhan,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10391856/10393862/10394259.pdf?arnumber=10394259
Reference19 articles.
1. An analysis of the requirements traceability problem
2. A Systematic Mapping Study of Information Retrieval Approaches Applied to Requirements Trace Recovery
3. A Literature Review of Automatic Traceability Links Recovery for Software Change Impact Analysis
4. Traceability Transformed: Generating More Accurate Links with Pre-Trained BERT Models
5. Semantically Enhanced Software Traceability Using Deep Learning Techniques
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. An empirical study on the state-of-the-art methods for requirement-to-code traceability link recovery;Journal of King Saud University - Computer and Information Sciences;2024-07
2. MLTracer: An Approach Based on Multi-Layered Gradient Boosting Decision Trees for Requirements Traceability Recovery;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
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