An Empirical Study on Source Code Feature Extraction in Preprocessing of IR-Based Requirements Traceability
Author:
Affiliation:
1. Wuhan Textile University,School of Computer Science and Artificial Intelligence,Wuhan,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10061851/10061812/10061813.pdf?arnumber=10061813
Reference18 articles.
1. A vector space model for automatic indexing
2. Requirements Traceability for Object Oriented Systems by Partitioning Source Code
3. Configuring Latent Semantic Indexing for Requirements Tracing
4. On the role of semantics in automated requirements tracing
5. Analyzing closeness of code dependencies for improving IR-based Traceability Recovery
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1. 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
2. A Systematic Mapping Study of Machine Learning Techniques Applied to Software Traceability;2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC);2023-10-01
3. DF4RT: Deep Forest for Requirements Traceability Recovery Between Use Cases and Source Code;2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC);2023-10-01
4. Chat-EasyEdu: An Education Field Intelligent Question-Answering System Based on Knowledge Graphs;2023 10th International Conference on Dependable Systems and Their Applications (DSA);2023-08-10
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