The SEOSS 33 dataset - requirements, bug reports, code history, and trace links for entire projects
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
In: Data in Brief(25, 2019)
- Classification
-
Biowissenschaften, Biologie
- Event
-
Veröffentlichung
- (where)
-
Ilmenau
- (who)
-
TU Ilmenau
- (when)
-
2019
- Creator
- DOI
-
10.1016/j.dib.2019.104005
- URN
-
urn:nbn:de:101:1-2020012002063232536958
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:45 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Rath, Michael
- Mäder, Patrick
- TU Ilmenau
Time of origin
- 2019
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The benefits of traceability during software implementation
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A definition-by-example approach and visual language for activity patterns in engineering disciplines
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Konzipierung und Realisierung eines Kommunikationsverfahrens zum Austausch von Modellierungsdaten und zur Erstellung und Aufrechterhaltung von Traceability zwischen verteilten Applikationen
Rule-based maintenance of post-requirements traceability
The benefits of traceability during software implementation
SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasks
Utilizing traceable software artifacts to improve bug localization
Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review
Synaptic scaling - an artificial neural network regularization inspired by nature
7 Dimensions of software change patterns
Multi-view classification with convolutional neural networks
Flipped Classroom: Effective Teaching for Time Series Forecasting
A definition-by-example approach and visual language for activity patterns in engineering disciplines
Integrating context-based recommendation with deep NN image classification for plant identification tasks