Requirements Classification for Traceability Link Recovery
| dc.contributor.author | Hey, Tobias | |
| dc.contributor.author | Keim, Jan | |
| dc.contributor.author | Corallo, Sophie | |
| dc.contributor.editor | Koziolek, Anne | |
| dc.contributor.editor | Lamprecht, Anna-Lena | |
| dc.contributor.editor | Thüm, Thomas | |
| dc.contributor.editor | Burger, Erik | |
| dc.date.accessioned | 2025-02-14T09:36:29Z | |
| dc.date.available | 2025-02-14T09:36:29Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | The paper assesses the potential of requirements classification approaches to identify parts of requirements that are irrelevant for automated traceability link recovery between requirements and code. We were able to show that automatic identification of parts of requirements that do not describe functional aspects can significantly improve the recovery performance and that the parts can be identified with an F1-score of 84 %. | en |
| dc.identifier.doi | 10.18420/se2025-25 | |
| dc.identifier.eissn | 2944-7682 | |
| dc.identifier.issn | 2944-7682 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/45786 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | Software Engineering 2025 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-360 | |
| dc.subject | Requirements Classification | |
| dc.subject | Traceability Link Recovery | |
| dc.subject | Requirements Engineering | |
| dc.subject | Machine Learning | |
| dc.subject | Information Retrieval | |
| dc.subject | Large Language Models (LLM) | |
| dc.title | Requirements Classification for Traceability Link Recovery | en |
| mci.conference.date | 22.-28. Februar 2025 | |
| mci.conference.location | Karlsruhe | |
| mci.conference.sessiontitle | Scientific Programme | |
| mci.reference.pages | 83-84 |
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