2025
PRE-PRINT - The Pursuit of Diversity: Multi-Objective Testing of Deep Reinforcement Learning Agents
SSBSE Co-Located with ASE
Authors: Antony Bartlett, Cynthia Liem, Annibale Panichella
Finding diverse test cases for deep reinforcement learning agents using multi-objective optimization
2025
PRE-PRINT - The Last Dependency Crusade: Solving Python Dependency Conflicts with LLMs
AgenticSE Co-Located with ASE
Authors: Antony Bartlett, Cynthia Liem, Annibale Panichella
Fixing Python dependency conflicts using large language models
2025
DRVN at the ICST 2025 Tool Competition – Self-Driving Car Testing Track
International Conference on Software Testing
Authors: Antony Bartlett, Cynthia Liem, Annibale Panichella
Tool competition entry for automated regression testing in self-driving car testing
2024
Position: Stop Making Unscientific AGI Performance Claims
International Conference on Machine Learning
Authors: Patrick Altmeyer*, Andrew M. Demetriou, Antony Bartlett, Cynthia C.S. Liem
Position paper on scientific rigor for AGI claims
2024
Danger is My Middle Lane: Simulations from Real-World Dangerous Roads
SSBSE Co-Located with ASE
Authors: Antony Bartlett, Annibale Panichella
Conference paper on autonomous vehicle testing in dangerous scenarios
2024
Multi-objective differential evolution in the generation of adversarial examples
Elsevier - Science of Computer Programming
Authors: Antony Bartlett, Cynthia Liem, Annibale Panichella
Journal publication on multi-objective optimization for adversarial examples
2023
On the Strengths of Pure Evolutionary Algorithms in Generating Adversarial Examples
SBFT Co-Located with ICSE
Authors: Antony Bartlett, Cynthia Liem, Annibale Panichella
Research on multi-objective evolutionary algorithms for adversarial example generation
🏆 Best Paper Award
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