Test Selection

Test selection aims to optimize the efficiency and effectiveness of testing by strategically choosing a subset of tests from a larger pool. Current research focuses on developing novel algorithms, including neural networks and machine learning models, to prioritize tests based on factors like fault detection potential, coverage improvement, and robustness to various data biases. These advancements are particularly impactful in high-stakes domains such as large language model verification, deep learning model testing, and video game quality assurance, where reducing testing time and cost is crucial. The ultimate goal is to improve the reliability and quality of software and AI systems while minimizing resource expenditure.

Papers

June 19, 2024

Detecting Stimuli with Novel Temporal Patterns to Accelerate Functional Coverage Closure
Xuan Zheng, Tim Blackmore, James Buckingham, Kerstin Eder
Temporal Pattern Coverage Criterion Visual Detection Test Selection Based Verification

April 14, 2024

Evaluation and Improvement of Fault Detection for Large Language Models
Qiang Hu, Jin Wen, Maxime Cordy, Yuheng Huang, Wei Ma, Xiaofei Xie, Lei Ma
Large Language Model Fault Detection Open Source LLM Deep Learning Testing Test Selection

August 10, 2023

Test-Time Selection for Robust Skin Lesion Analysis
Alceu Bissoto, Catarina Barata, Eduardo Valle, Sandra Avila
Spurious Correlation Annotated Image Skin Lesion Analysis Test Selection Skin Lesion Classification

July 29, 2023

Evaluating the Robustness of Test Selection Methods for Deep Neural Networks
Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Wei Ma, Mike Papadakis, Yves Le Traon
Deep Neural Network Native Robustness Fault Detection Test Data Test Selection

March 8, 2023

DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks
Zohreh Aghababaeyan, Manel Abdellatif, Mahboubeh Dadkhah, Lionel Briand
Deep Neural Network Model Retraining DNN Testing Test Selection

July 1, 2022

Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure
Xuan Zheng, Kerstin Eder, Tim Blackmore
Neural Network Coverage Criterion Test Selection Based Verification

March 10, 2022

SUPERNOVA: Automating Test Selection and Defect Prevention in AAA Video Games Using Risk Based Testing and Machine Learning
Alexander Senchenko, Naomi Patterson, Hamman Samuel, Dan Isper
Machine Learning Game Testing Test Selection Quality Assurance

Test Selection

Papers

Detecting Stimuli with Novel Temporal Patterns to Accelerate Functional Coverage Closure

Evaluation and Improvement of Fault Detection for Large Language Models

Test-Time Selection for Robust Skin Lesion Analysis

Evaluating the Robustness of Test Selection Methods for Deep Neural Networks

DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks

Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure

SUPERNOVA: Automating Test Selection and Defect Prevention in AAA Video Games Using Risk Based Testing and Machine Learning