Towards a Domain-Specific Modeling Language for Streamlined Change Management in AI Systems Development
Requirements in AI systems are rarely fixed at the start of a project, as changes are often necessary—and sometimes unavoidable—due to traditional factors and AI-specific challenges such as evolving data patterns and model retraining needs. Managing these changes effectively is essential to ensure system reliability and project success. However, current AI development practices typically separate the development pipelines for ML and non-ML components, creating a lack of systematic coordination when changes occur. This fragmentation often leads to inconsistencies, overlooked impacts, and error propagation across pipelines. To address these challenges, this paper presents an approach to streamline requirements change management using process modeling techniques. By providing a cohesive framework, our approach enables stakeholders to identify and address critical changes early in the development lifecycle, minimizing risks and preventing errors from reaching production.
Mon 28 AprDisplayed time zone: Eastern Time (US & Canada) change
14:00 - 15:30 | Quality Assurance for AI systemsResearch and Experience Papers at 208 Chair(s): Eduardo Santana de Almeida Federal University of Bahia | ||
14:00 10mTalk | Towards a Domain-Specific Modeling Language for Streamlined Change Management in AI Systems Development Research and Experience Papers Razan Abualsaud IRIT, CNRS, Toulouse | ||
14:10 15mTalk | An AI-driven Requirements Engineering Framework Tailored for Evaluating AI-Based Software Research and Experience Papers Hamed Barzamini , Fatemeh Nazaritiji Northern Illinois University, Annalise Brockmann Northern Illinois University, Hasan Ferdowsi Northern Illinois university, Mona Rahimi Northern Illinois University | ||
14:25 15mTalk | MLScent: A tool for Anti-pattern detection in ML projects Research and Experience Papers | ||
14:40 15mTalk | Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)Distinguished paper Award Candidate Research and Experience Papers Boyue Caroline Hu University of Toronto, Divya Gopinath KBR; NASA Ames, Ravi Mangal Colorado State University, Nina Narodytska VMware Research, Corina S. Păsăreanu Carnegie Mellon University, Susmit Jha SRI | ||
14:55 15mTalk | Investigating Issues that Lead to Code Technical Debt in Machine Learning Systems Research and Experience Papers Rodrigo Ximenes Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Antonio Pedro Santos Alves Pontifical Catholic University of Rio de Janeiro, Tatiana Escovedo Pontifical Catholic University of Rio de Janeiro, Rodrigo Spinola Virginia Commonwealth University, Marcos Kalinowski Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Pre-print | ||
15:10 10mTalk | Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge Research and Experience Papers Santiago del Rey Universitat Politècnica De Catalunya - Barcelona Tech, Adrià Medina Universitat Politècnica de Barcelona - BarcelonaTech (UPC), Xavier Franch Universitat Politècnica de Catalunya, Silverio Martínez-Fernández UPC-BarcelonaTech Pre-print | ||
15:20 10mOther | Discussion Research and Experience Papers |