Clustering-based Automated Requirement Trace Retrieval

The benefits of requirement traceability are well known and documented. The traceability links between requirements and code are fundamental in supporting different activities in the software development process, including change management and software maintenance. These links can be obtained using manual or automatic means. Manual trace retrieval is a time-consuming task. Automatic trace retrieval can be performed via various tools such as Information retrieval or machine learning techniques. Meanwhile, a big concern associated with automated trace retrieval is the low precision problem primarily caused by the term mismatches across documents to be traced. This study proposes an approach that addresses the term mismatch problem to obtain the greatest improvements in the trace retrieval accuracy. The proposed approach uses clustering in the automated trace retrieval process and performs an experimental evaluation against previous benchmarks. The results show that the proposed approach improves the trace retrieval precision.

@article{Al-walidi2022,
  title     = {Clustering-based Automated Requirement Trace Retrieval},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {12},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Nejood Hashim Al-walidi and Shahira Shaaban Azab and Abdelaziz Khamis and Nagy Ramadan Darwish},
  doi       = {10.14569/IJACSA.2022.0131292},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131292}
}