Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning

Hemanth Gudaparthi; Nan Niu; Boyang Wang; Tanmay Bhowmik; Hui Liu; Jianzhang Zhang; Juha Savolainen; Glen Horton; Sean Crowe; Thomas Scherz; Lisa Haitz

doi:10.1109/RE57278.2023.00022

Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning

Hemanth Gudaparthi
, Nan Niu
, Boyang Wang
, Tanmay Bhowmik
, Hui Liu
, Jianzhang Zhang
, Juha Savolainen
, Glen Horton
, Sean Crowe
, Thomas Scherz
, Lisa Haitz

Governors State University
Mississippi State University

Research output: Chapter or Contribution to Book › Literary contribution

Original language	English
Title of host publication	Proceedings - 31st IEEE International Requirements Engineering Conference, RE 2023
Editors	Kurt Schneider, Fabiano Dalpiaz, Jennifer Horkoff
Pages	134-145
Number of pages	12
ISBN (Electronic)	9798350326895
DOIs	https://doi.org/10.1109/RE57278.2023.00022
State	Published - 2023
Externally published	Yes
Event	31st IEEE International Requirements Engineering Conference, RE 2023 - Hannover, Germany Duration: Sep 4 2023 → Sep 8 2023

Publication series

Name	Proceedings of the IEEE International Conference on Requirements Engineering
Volume	2023-September
ISSN (Print)	1090-705X
ISSN (Electronic)	2332-6441

Conference

Conference	31st IEEE International Requirements Engineering Conference, RE 2023
Country/Territory	Germany
City	Hannover
Period	9/4/23 → 9/8/23

ASJC Scopus Subject Areas

General Computer Science
General Engineering
Strategy and Management

Keywords

adversarial examples
automated requirements generation
creative requirements
deep learning

Access to Document

10.1109/RE57278.2023.00022

Cite this

Gudaparthi, H., Niu, N., Wang, B., Bhowmik, T., Liu, H., Zhang, J., Savolainen, J., Horton, G., Crowe, S., Scherz, T., & Haitz, L. (2023). Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning. In K. Schneider, F. Dalpiaz, & J. Horkoff (Eds.), Proceedings - 31st IEEE International Requirements Engineering Conference, RE 2023 (pp. 134-145). (Proceedings of the IEEE International Conference on Requirements Engineering; Vol. 2023-September). https://doi.org/10.1109/RE57278.2023.00022

Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning. / Gudaparthi, Hemanth; Niu, Nan; Wang, Boyang et al.
Proceedings - 31st IEEE International Requirements Engineering Conference, RE 2023. ed. / Kurt Schneider; Fabiano Dalpiaz; Jennifer Horkoff. 2023. p. 134-145 (Proceedings of the IEEE International Conference on Requirements Engineering; Vol. 2023-September).

Research output: Chapter or Contribution to Book › Literary contribution

Gudaparthi, H, Niu, N, Wang, B, Bhowmik, T, Liu, H, Zhang, J, Savolainen, J, Horton, G, Crowe, S, Scherz, T & Haitz, L 2023, Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning. in K Schneider, F Dalpiaz & J Horkoff (eds), Proceedings - 31st IEEE International Requirements Engineering Conference, RE 2023. Proceedings of the IEEE International Conference on Requirements Engineering, vol. 2023-September, pp. 134-145, 31st IEEE International Requirements Engineering Conference, RE 2023, Hannover, Germany, 9/4/23. https://doi.org/10.1109/RE57278.2023.00022

Gudaparthi H, Niu N, Wang B, Bhowmik T, Liu H, Zhang J et al. Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning. In Schneider K, Dalpiaz F, Horkoff J, editors, Proceedings - 31st IEEE International Requirements Engineering Conference, RE 2023. 2023. p. 134-145. (Proceedings of the IEEE International Conference on Requirements Engineering). doi: 10.1109/RE57278.2023.00022

Gudaparthi, Hemanth ; Niu, Nan ; Wang, Boyang et al. / Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning. Proceedings - 31st IEEE International Requirements Engineering Conference, RE 2023. editor / Kurt Schneider ; Fabiano Dalpiaz ; Jennifer Horkoff. 2023. pp. 134-145 (Proceedings of the IEEE International Conference on Requirements Engineering).

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Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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