Business processes are commonly represented by modelling languages, such as Event-driven Process Chain (EPC), Yet Another Workflow Language (YAWL), and the most popular standard notation for modelling business processes, the Business Process Model and Notation (BPMN). Most recently, chatbots, programs that allow users to interact with a machine using natural language, have been increasingly used for business process execution support. A recent category of chatbots worth mentioning is generative-based chatbots, powered by Large Language Models (LLMs) such as OpenAI’s Generative Pre-Trained Transformer (GPT) model and Google’s Pathways Language Model (PaLM), which are trained on billions of parameters and support conversational intelligence. However, it is not clear whether generative-based chatbots are able to understand and meet the requirements of constructs such as those provided by BPMN for process execution support. This paper presents a case study to compare the performance of prominent generative models, GPT and PaLM, in the context of process execution support. The research sheds light into the challenging problem of using conversational approaches supported by generative chatbots as a means to understand process-aware modelling notations and support users to execute their tasks.

Software development is a collaborative effort in which developers often share knowledge by interacting with artifacts and among themselves. When developers interact with artifacts, what we call a Developer-Artifact interaction, they access or define pieces of information within artifacts. When they interact among, what we call a Developer-Developer interaction, they exchange information using a collaborative platform to clarify an issue, promote an idea, or share a comment. However, the high number of such interactions makes it very difficult to capture and assess the evolution of the developers' knowledge about specific software project artifacts and tasks. On one hand, the knowledge they have decreases over time due to the natural limitations of human cognition that restrict their capabilities to cope with information overload. On the other hand, the more they know about specific project elements, the more they are apt to collaborate. In this paper we describe ongoing work on knowledge-oriented and graph-based models that capture the evolution of developers’ knowledge about software project elements such as artifacts, tasks, similar tasks, and the whole software project, and explore the associated rich project-related connected networks.