Arnaud Gotlieb

About

Arnaud Gotlieb is a prominent researcher specializing in software testing, constraint programming, and the verification of cyber-physical and robotic systems. His work sits at a compelling intersection of formal methods and industrial software engineering, with a particular focus on applying constraint programming techniques to automate and improve test generation for complex, real-world systems. Gotlieb is perhaps best known for his sustained collaboration with ABB Robotics, where he pioneered the use of constraint programming to test industrial paint control systems — work that produced multiple influential publications between 2013 and 2017, accumulating nearly 70 citations collectively. His most cited paper, "Testing robot controllers using constraint programming and continuous integration" (2014, 32 citations), demonstrated how rigorous formal testing approaches could be integrated into industrial development pipelines, bridging academic methodology and commercial practice. Beyond robotics, Gotlieb has expanded his research to address emerging challenges in artificial intelligence, including comparative testing of deep learning models (2022) and metamorphic testing of human trajectory prediction systems (2024), reflecting his adaptability to evolving technological frontiers. His body of work consistently emphasizes practical, scalable testing solutions for safety-critical systems, making his research particularly valuable to engineers and academics working at the cutting edge of autonomous systems and intelligent robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Testing robot controllers using constraint programming and continuous integration

   32 citations · 2014
2. 2
   Using CP in Automatic Test Generation for ABB Robotics’ Paint Control System

   17 citations · 2014
3. 3
   Time-Aware Test Case Execution Scheduling for Cyber-Physical Systems

   14 citations · 2017
4. 4
   Testing Robotized Paint System Using Constraint Programming: An Industrial Case Study

   10 citations · 2014
5. 5
   Test Generation for Robotized Paint Systems Using Constraint Programming in a Continuous Integration Environment

   9 citations · 2013
6. 6
   Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques

   9 citations · 2022
7. 7
   Stress Testing of Single-Arm Robots Through Constraint-Based Generation of Continuous Trajectories

   4 citations · 2019
8. 8
   Testing Industrial Robotic Systems: A New Battlefield!

   4 citations · 2020
9. 9
   Evaluating Human Trajectory Prediction with Metamorphic Testing

   2 citations · 2024
10. 10
    Deploying Constraint Programming for Testing ABB's Painting Robots

    2 citations · 2017

Key Collaborators

Contact & Links