Systematic Testing

Systematic testing aims to rigorously evaluate the performance and robustness of systems, focusing on efficiency and accuracy in assessment. Current research emphasizes developing adaptive testing methods, leveraging techniques like Item Response Theory and deep reinforcement learning to optimize testing processes and prioritize critical areas, such as changes in software updates or vulnerabilities in machine learning models. This work is crucial for ensuring the reliability and safety of various applications, from educational software and grammatical error correction to the deployment of robust AI systems in safety-critical domains.

Papers

September 24, 2024

Implicit assessment of language learning during practice as accurate as explicit testing
Jue Hou, Anisia Katinskaia, Anh-Duc Vu, Roman Yangarber
Human Language Practice Mode Item Response Theory Implicit Feedback Systematic Testing

April 23, 2024

Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models
Kostiantyn Omelianchuk, Andrii Liubonko, Oleksandr Skurzhanskyi, Artem Chernodub, Oleksandr Korniienko, Igor Samokhin
Large Language Model Grammatical Error Correction Ranking Model Current Approach Pillar Domain Systematic Testing

September 4, 2023

Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning
Chao Peng, Zhengwei Lv, Jiarong Fu, Jiayuan Liang, Zhao Zhang, Ajitha Rajan, Ping Yang
Deep Reinforcement Learning Level Test Mobile Application Deep Learning Testing Based Testing Systematic Testing

July 17, 2023

Systematic Testing of the Data-Poisoning Robustness of KNN
Yannan Li, Jingbo Wang, Chao Wang
Poisoning Attack Data Poisoning Attack K Nearest Neighbor Truly Robust Case Systematic Testing

April 17, 2022

Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning
Jun Guo, Yonghong Chen, Yihang Hao, Zixin Yin, Yin Yu, Simin Li
Native Robustness MARL Algorithm Systematic Testing

Systematic Testing

Papers

Implicit assessment of language learning during practice as accurate as explicit testing

Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models

Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning

Systematic Testing of the Data-Poisoning Robustness of KNN

Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning