Keywords: API classification, BERT, GPT, OpenAPI Specification
Astract: Application Programming Interfaces (APIs) enable interaction, integration, and interoperability among applications and services, contributing to their adoption and proliferation. However, discovering APIs has relied on manual, time-consuming, costly processes that jeopardize their reuse potential and accentuate the need for effective API retrieval mechanisms. Leveraging the OpenAPI Specification as a basis, this paper presents an exploratory study that combines BERT and GPT machine learning models to propose a novel API classifier. Our investigation explored the zero-shot learning capabilities of GPT-4 and GPT-3.5 using relevant terms extracted from API descriptions using BERT. The evaluation of our approach on two datasets comprising 940 API descriptions sourced from public repositories yielded an F1-score of 100% in the small dataset (17 APIs) and 39.1% in the large dataset (923 APIs). These results surpass state-of-the-art on the small dataset with an impressive 29-point improvement. The large dataset showed GPT can suggest labels not in the provided list. Manual analysis revealed that GPT’s suggested labels fit the API intent better in 18 out of 20 cases, highlighting its potential for unknown classes and mismatch detection. This emphasizes the need to improve dataset quality and availability for API research. Our findings show the potential of automated API retrieval and open avenues for future research.

Keywords: Conceptual Modeling, Sustainability

Keywords: Conceptual modeling, Domain modeling, Generative AI, LLM, UML
Astract: The information systems engineering community is increasingly exploring the use of Large Language Models (LLMs) for a variety of tasks, including domain modeling, business process modeling, software modeling, and systems modeling. However, most existing research remains exploratory and lacks a systematic approach to analyzing the impact of prompt content on model quality. This paper seeks to fill this gap by investigating how different levels of description granularity (whole text vs. paragraph-by-paragraph) and modeling strategies (model-based vs. list-based) affect the quality of LLM-generated domain models. Specifically, we conducted an experiment with two state-of-the-art LLMs (GPT-4o and Llama-3.1-70b-versatile) on tasks involving use case and class modeling. Our results reveal challenges that extend beyond the chosen granularity, strategy, and LLM, emphasizing the importance of human modelers not only in crafting effective prompts but also in identifying and addressing critical aspects of LLM-generated models that require refinement and correction.

Keywords: Agility, Business Process Management, Conversational Agents, LLM, Social media
Astract: The traditional approaches in dynamic and collaborative environments that use Business Process Management (BPM) methodologies usually lack the ability to adapt to real-time changes in case of heavy human involvement in repetitive processes. The agility of social BPM is, however, still limited because of a lack of context-sensitive tool support. This paper proposes a mapping framework that leverages conversational AI agents on a social media platform to enhance BPM agility. AI-driven conversational agents are mapped to the respective phases of the BPM lifecycle to provide real-time guidance, recommendations, and context-sensitive feedback. The agents’ collaborative features enable inclusive co-construction, interactive task execution, and continuous monitoring of the processes. That allows dynamic adaptation of the processes in case of changes so that tasks remain aligned with the users’ needs and contextual demands. This framework is developed through an exploratory approach that integrates literature review, deductive design, and use case-based evaluation. This framework could bridge gaps in the current BPM practices by integrating BPM, AI, and social media, thereby offering a new model for agile and collaborative business process management.

Keywords: Conceptual Modeling, Multimodal data, Model Enrichment