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About

Dmytro Humeniuk is a researcher specializing in search-based software testing, autonomous systems verification, and cyber-physical systems engineering. His work focuses on developing automated frameworks that generate challenging test scenarios for safety-critical systems — including self-driving cars, autonomous robots, drones, and robotic manipulators — where undetected failures carry serious real-world consequences. Humeniuk's most significant contribution is AmbieGen, a versatile search-based testing framework designed to rigorously evaluate autonomous systems during model-in-the-loop testing stages. First introduced in the context of cyber-physical systems in 2022 (28 citations) and subsequently extended to broader autonomous systems applications, AmbieGen has become a recognized tool in the automated testing community. His more recent research pushes this work further by integrating reinforcement learning with evolutionary search strategies, improving computational efficiency without sacrificing test quality — a meaningful advance given the high cost of simulator-based evaluations. He has also tackled the nuanced challenge of testing deep learning vision models embedded within robotic manipulators, addressing complex software interactions between perception and control pipelines. With nearly 50 citations across a focused and rapidly growing body of work, Humeniuk is an emerging voice in autonomous systems testing, offering practical, scalable solutions to one of modern software engineering's most pressing challenges.