1 to 7 of 7 Results
Dec 9, 2024
Pesl, Robin D.; Mathew, Jerin George; Mecella, Massimo; Aiello, Marco, 2024, "Replication Data for: Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation", https://doi.org/10.18419/DARUS-4605, DaRUS, V1
Integrating multiple (sub-)systems is essential to create advanced Information Systems (ISs). Difficulties mainly arise when integrating dynamic environments across the IS lifecycle, e.g., services not yet existent at design time. A traditional approach is a registry that provides the API documentation of the systems’ endpoints. Large Language Mode... |
Oct 7, 2024
Pesl, Robin D.; Mombrey, Carolin; Klein, Kevin; Georgievski, Ilche; Becker, Steffen; Herzwurm, Georg; Aiello, Marco, 2024, "Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing", https://doi.org/10.18419/DARUS-4497, DaRUS, V1
A classic, central Service-Oriented Computing (SOC) challenge is the service composition problem. It concerns solving a user-defined task by selecting a suitable set of services, possibly found at runtime, determining an invocation order, and handling request and response parameters. The solutions proposed in the past two decades mostly resort to a... |
Pesl, Robin D.; Stötzner, Miles; Georgievski, Ilche; Aiello, Marco, 2023, "Replication Data for: Uncovering LLMs for Service-Composition: Challenges and Opportunities", https://doi.org/10.18419/DARUS-3767, DaRUS, V1, UNF:6:GV+GzTPs7xXW9ITeS6uC7Q== [fileUNF]
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: C... |
Nov 30, 2022
Falazi, Ghareeb; Breitenbücher, Uwe; Leymann, Frank; Schulte, Stefan, 2022, "Quality Assurance Forms for Grey Literature Studies Supporting Cross-chain Smart Contract Invocations", https://doi.org/10.18419/DARUS-3283, DaRUS, V1
Forms that estimate the quality of candidate grey literature studies to be included in the multi-vocal literature review entitled, "Cross-Chain Smart Contract Invocations: A Systematic Multi-Vocal Literature Review". |
Nov 30, 2022
Falazi, Ghareeb; Breitenbücher, Uwe; Leymann, Frank; Schulte, Stefan, 2022, "Selection, Data Extraction, and Data Synthesis for Cross-chain Smart Contract Invocation Approaches", https://doi.org/10.18419/DARUS-3282, DaRUS, V1
This document contains information about: All the studies that were initially accepted for inclusion in the Multi-vocal Systematic Literature review. The studies that were rejected in the second phase and the reasons for their rejection. Filled out data extraction form for the finally-selected studies. The analysis of the extracted data by applying... |
Nov 30, 2022
Falazi, Ghareeb; Breitenbücher, Uwe; Leymann, Frank; Schulte, Stefan, 2022, "Qualitative Visual Analysis of Cross-chain Smart Contract Invocation Approaches", https://doi.org/10.18419/DARUS-3281, DaRUS, V1
This document demonstrates the visual qualitative analysis performed over the approaches systematically selected for inclusion in the study entitled, "Cross-Chain Smart Contract Invocations: A Systematic Multi-Vocal Literature Review". |
Nov 30, 2022
Falazi, Ghareeb; Breitenbücher, Uwe; Leymann, Frank; Schulte, Stefan, 2022, "Cross-chain Smart Contract Invocations - a Multi-vocal Literature Review Protocol", https://doi.org/10.18419/DARUS-3280, DaRUS, V1
This document introduces the protocol that describes how a Multivocal Literature Review (MLR) regarding Cross-chain Smart Contract Invocation (CCSCI) was planned to be conducted. The main purpose of having this protocol is to avoid bias when selecting primary sources and processing them. Furthermore, it helped co-authors gain an overview of the top... |
