CAIN 2025
Sun 27 - Mon 28 April 2025 Ottawa, Ontario, Canada
co-located with ICSE 2025
Mon 28 Apr 2025 14:40 - 14:55 at 208 - Quality Assurance for AI systems Chair(s): Eduardo Santana de Almeida

Debugging of Deep Neural Networks (DNNs), particularly vision models, is very challenging due to the complex and opaque decision-making processes in these networks. In this paper, we explore multi-modal Vision-Language Models (VLMs), such as CLIP, to automatically interpret the opaque representation space of vision models using natural language. This in turn, enables a semantic analysis of model behavior using human-understandable concepts, without requiring costly human annotations. Key to our approach is the notion of semantic heatmap, that succinctly captures the statistical properties of DNNs in terms of the concepts discovered with the VLM and that are computed off-line using a held-out data set. We show the utility of semantic heatmaps for fault localization – an essential step in debugging – in vision models. Our proposed technique helps localize the fault in the network (encoder vs head) and also highlights the responsible high-level concepts, by leveraging novel differential heatmaps, which summarize the semantic differences between the correct and incorrect behavior of the analyzed DNN. We further propose a lightweight runtime analysis to detect and filter-out defects at runtime, thus improving the reliability of the analyzed DNNs. The runtime analysis works by measuring and comparing the similarity between the heatmap computed for a new (unseen) input and the heatmaps computed a-priori for correct vs incorrect DNN behavior. We consider two types of defects: misclassifications and vulnerabilities to adversarial attacks. We demonstrate the debugging and runtime analysis on a case study involving a complex ResNet-based classifier trained on the RIVAL-10 dataset.

Mon 28 Apr

Displayed 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
10m
Talk
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
15m
Talk
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
15m
Talk
MLScent: A tool for Anti-pattern detection in ML projects
Research and Experience Papers
Karthik Shivashankar University of Oslo, Antonio Martini University of Oslo
14:40
15m
Talk
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
15m
Talk
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
10m
Talk
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
10m
Other
Discussion
Research and Experience Papers

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