%0 Generic %1 pesl2023replication %A Pesl, Robin D. %A Stötzner, Miles %A Georgievski, Ilche %A Aiello, Marco %D 2023 %K %R 10.18419/darus-3767 %T Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities %X Experimental results for the ICSOC 2023 AI-PA position paper Üncovering LLMs for Service-Composition: Challenges and Opportunities. "Exemplars: List of scenarios found in the Google Scholar literature search.Experiment 1 Service Discovery: Chat history for experiment 1 asking ChatGPT for existing real services. Experiment 2 Service Composition: Chat history and service composition for experiment 2 asking ChatGPT for a service composition in Python using a natural language task and the list of services from experiment 1. Experiment 3 Combined Service Discovery and Composition: Chat history and service composition for experiment 3 asking ChatGPT for a service composition in Python using a natural language task without a list of services. Each experiment in the dataset has its own folder (use the tree view to see the folder layout of the files). Chats in experiments 2 and 3 are accompanied by their service composition in Python from that chat as an extra file.

@misc{pesl2023replication, abstract = {Experimental results for the ICSOC 2023 AI-PA position paper "Uncovering LLMs for Service-Composition: Challenges and Opportunities. "Exemplars: List of scenarios found in the Google Scholar literature search.Experiment 1 Service Discovery: Chat history for experiment 1 asking ChatGPT for existing real services. Experiment 2 Service Composition: Chat history and service composition for experiment 2 asking ChatGPT for a service composition in Python using a natural language task and the list of services from experiment 1. Experiment 3 Combined Service Discovery and Composition: Chat history and service composition for experiment 3 asking ChatGPT for a service composition in Python using a natural language task without a list of services. Each experiment in the dataset has its own folder (use the tree view to see the folder layout of the files). Chats in experiments 2 and 3 are accompanied by their service composition in Python from that chat as an extra file. }, added-at = {2023-12-14T10:28:49.000+0100}, affiliation = {Pesl, Robin D./University of Stuttgart, Institute of Architecture of Application Systems, Stötzner, Miles/University of Stuttgart, Institute of Software Engineering, Georgievski, Ilche/University of Stuttgart, Institute of Architecture of Application Systems, Aiello, Marco/University of Stuttgart, Institute of Architecture of Application Systems}, author = {Pesl, Robin D. and Stötzner, Miles and Georgievski, Ilche and Aiello, Marco}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2a89c24f47a54e08095f95b79c583af04/unibiblio}, doi = {10.18419/darus-3767}, howpublished = {Dataset}, interhash = {0619f15f280e27d46c8e4a92f7414b79}, intrahash = {a89c24f47a54e08095f95b79c583af04}, keywords = {}, note = {Related to: Pesl, R.D., Stötzner, M., Georgievski, I., Aiello, M.: Uncovering LLMs for Service-Composition: Challenges and Opportunities. In: ICSOC 2023 Workshops (2023)}, orcid-numbers = {Pesl, Robin D./0000-0002-5980-9395, Stötzner, Miles/0000-0003-1538-5516, Georgievski, Ilche/0000-0001-6745-0063, Aiello, Marco/0000-0002-0764-2124}, timestamp = {2023-12-14T09:28:49.000+0100}, title = {Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities}, year = 2023 }