TY - GEN
T1 - Toward improved artificial intelligence in requirements engineering
T2 - 27th IEEE International Requirements Engineering Conference Workshops, REW 2019
AU - Hayes, Jane Huffman
AU - Payne, Jared
AU - Leppelmeier, Mallory
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Data is the driver of artificial intelligence in requirements engineering. While some applications may lend themselves to training sets that are easily accessible (such as sentiment detection, feature request classification, requirements prioritization), other tasks face data challenges. Tracing and domain model building are examples of applications where data is not easily found or in the proper format or with the necessary metadata to support deep learning, machine learning, or other artificial intelligence techniques. This paper surveys datasets available from sources such as the Center of Excellence for Software and Systems Traceability and provides valuable metadata that can be used by re-searchers or practitioners when deciding what datasets to use, what aspects of datasets to use, what features to use in deep learning, and more.
AB - Data is the driver of artificial intelligence in requirements engineering. While some applications may lend themselves to training sets that are easily accessible (such as sentiment detection, feature request classification, requirements prioritization), other tasks face data challenges. Tracing and domain model building are examples of applications where data is not easily found or in the proper format or with the necessary metadata to support deep learning, machine learning, or other artificial intelligence techniques. This paper surveys datasets available from sources such as the Center of Excellence for Software and Systems Traceability and provides valuable metadata that can be used by re-searchers or practitioners when deciding what datasets to use, what aspects of datasets to use, what features to use in deep learning, and more.
KW - Artificial intelligence
KW - Datasets
KW - Deep learning
KW - Machine learning
KW - Metadata
KW - Requirement engineering
KW - Training sets
UR - https://www.scopus.com/pages/publications/85077981411
UR - https://www.scopus.com/pages/publications/85077981411#tab=citedBy
U2 - 10.1109/REW.2019.00052
DO - 10.1109/REW.2019.00052
M3 - Conference contribution
AN - SCOPUS:85077981411
T3 - Proceedings - 2019 IEEE 27th International Requirements Engineering Conference Workshops, REW 2019
SP - 256
EP - 262
BT - Proceedings - 2019 IEEE 27th International Requirements Engineering Conference Workshops, REW 2019
Y2 - 23 September 2019 through 27 September 2019
ER -