Dmytro Humeniuk

QA-enhanced development of intelligent robotic manipulators in Isaac Sim

In collaboration with Sycodal, in this project we develop reliable and robust robotic manipulators containing ML components. The approach is implemented in the NVIDIA Isaac Sim simulation environment.

Combining Reinforcement learning and Evolutionary search for generating test scenes

Co-authors: Prof. Khomh, Prof. Antoniol

In this project we use Reinforcement learning to initialize some part of a genetic algorithm to improve the initial population and increase the efficiency of the search for useful test scenes. We condunct experiments on the RL-controlled robotic ant as well as on an autonomous vehicle lane-keeping assist system.

Paper   Code

A framework for generating test scenes for autonomous cyber-physical systems with evolutionary search algorithms

Co-authors: Prof. Khomh, Prof. Antoniol

I this project we develop a tool for generating test-scenes for autonomous cyber-physical systems using evolutionary algorithms. Our tool starts with a population of randomly generated test scenes and evolves them, promoting the test scenes that reveal failures of the system under test. Refer to the approach paper for a more detailed description of the algorithm and to the tool paper for the description of the tool software.

  Approach Paper     Tool Paper   Code

Participation in the SBST Cyber-Physical Systems Testing Tool Competition

Co-authors: Prof. Khomh, Prof. Antoniol

This is a tool competition, where competitors should propose a test generator that produces virtual roads to test a vehicle lane keeping assist system. The aim of the generation is to produce diverse failure-inducing tests, i.e., roads that make the lane keeping assist system drive out of the lane. The ranking of the tools is based on coverage which measures the number of failed tests and their diversity. In 2022, our tool AmbieGen has won the competion.

Our submissions: 2021 ( ),  2022 (1st place ),  2023 (2nd place ) 2024 (2nd place )

Reinforcement Learning Informed Evolutionary Search for Autonomous Systems Testing

Humeniuk, Dmytro, Foutse Khomh, and Giuliano Antoniol. "Reinforcement learning informed evolutionary search for autonomous systems testing." arXiv preprint arXiv:2308.12762 (2023).

AmbieGen: A search-based framework for autonomous systems testing

Dmytro Humeniuk, Foutse Khomh, Giuliano Antoniol, AmbieGen: A search-based framework for autonomous systems testing, Science of Computer Programming, Volume 230, 2023, 102990, ISSN 0167-6423, https://doi.org/10.1016/j.scico.2023.102990.

A search-based framework for automatic generation of testing environments for cyber-physical systems

Humeniuk, Dmytro, Foutse Khomh, and Giuliano Antoniol. "A search-based framework for automatic generation of testing environments for cyber–physical systems." Information and Software Technology 149 (2022): 106936.