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Tue 11 Oct 2022 10:50 - 11:10 at Ballroom C East - Technical Session 1 - AI for SE I Chair(s): Andrea Stocco

Visualization is very important for machine learning (ML) pipelines because it can show explorations of the data to inspire data scientists and show explanations of the pipeline to improve understandability and trust. In this paper, we present a novel approach that automatically generates visualizations for ML pipelines by learning visualizations from highly-voted Kaggle pipelines. The solution extracts both code and dataset features from these high-quality human-written pipelines and corresponding training datasets, learns the mapping rules from code and dataset features to visualizations using association rule mining (ARM), and finally uses the learned rules to predict visualizations for unseen ML pipelines. The evaluation results show that the proposed solution is feasible and effective to generate visualizations for ML pipelines.

Tue 11 Oct

Displayed time zone: Eastern Time (US & Canada) change

10:30 - 12:30
Technical Session 1 - AI for SE IResearch Papers / Industry Showcase at Ballroom C East
Chair(s): Andrea Stocco Università della Svizzera italiana (USI)
10:30
20m
Research paper
B-AIS: An Automated Process for Black-box Evaluation of AI-enabled Software Systems against Domain Semantics
Research Papers
Hamed Barzamini , Mona Rahimi Northern Illinois University
10:50
20m
Industry talk
Automatic Generation of Visualizations for Machine Learning Pipelines
Industry Showcase
Lei Liu Fujitsu Laboratories of America, Inc., Wei-Peng Chen Fujitsu Research of America, Inc., Mehdi Bahrami Fujitsu Laboratories of America, Inc., Mukul Prasad Amazon Web Services
11:10
20m
Research paper
SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft DesignVirtual
Research Papers
Houssem Ben Braiek École Polytechnique de Montréal, Ali Tfaily Bombardier Aerospace, Foutse Khomh Polytechnique Montréal, Thomas Reid , Ciro Guida Bombardier Aerospace
Pre-print
11:30
20m
Research paper
Unveiling Hidden DNN Defects with Decision-Based Metamorphic TestingVirtual
Research Papers
Yuanyuan Yuan The Hong Kong University of Science and Technology, Qi Pang HKUST, Shuai Wang Hong Kong University of Science and Technology
11:50
20m
Research paper
Patching Weak Convolutional Neural Network Models through Modularization and CompositionVirtual
Research Papers
Binhang Qi Beihang University, Hailong Sun Beihang University, Xiang Gao Beihang University, China, Hongyu Zhang University of Newcastle
12:10
20m
Research paper
Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software DeploymentVirtual
Research Papers
Jie Zhu Peking University, Leye Wang Peking University, Xiao Han Shanghai University of Finance and Economics
DOI Pre-print