An AI-driven Requirements Engineering Framework Tailored for Evaluating AI-Based Software
Requirements Engineering (RE) has been extensively refined for traditional software systems, but AI-based software (AIS)\footnote{In this work, AI-based software (AIS) refers to software that relies exclusively on vision-based perception, meaning its understanding of the environment is derived solely from camera input.} introduces unique challenges that necessitate novel approaches.
This paper addresses the gap in RE practices for AIS by proposing a novel framework that adopts domain analysis and ontology creation from RE but adapts them for AIS by using the methods in eXplainable AI (XAI). The purpose of this framework is to demonstrate that systematically engineering AIS, according to RE practices, rather than fully relying on AI capabilities, will enhance the perception capabilities of resultant AIS.
This work aims to enhance RE4AI by offering a structured approach for managing and evaluating requirements specifications in AIS, ultimately leading to improved performance in these systems. Evaluation results showed that our framework improves AIS perception of variants of two domain concepts—pedestrian and aircraft—within the automotive and aviation domains.
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 |