activity
20232025
most citedTesting for Fault Diversity in Reinforcement Learning

7 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2025

Efficiently Ranking Software Variants with Minimal Benchmarks

Théo Matricon, Mathieu Acher, Helge Spieker +1

Benchmarking is a common practice in software engineering to assess the qualities and performance of software variants, coming from multiple competing systems or from configuration…

cs.SE2025★ 1 cited

Prompting for Performance: Exploring LLMs for Configuring Software

Helge Spieker, Théo Matricon, Nassim Belmecheri +7

Software systems usually provide numerous configuration options that can affect performance metrics such as execution time, memory usage, binary size, or bitrate. On the one hand,…

cs.SE2025★ 3 cited

Policy Testing with MDPFuzz (Replicability Study)

Quentin Mazouni, Helge Spieker, Arnaud Gotlieb +1

In recent years, following tremendous achievements in Reinforcement Learning, a great deal of interest has been devoted to ML models for sequential decision-making. Together with t…

cs.SE2024★ 7 cited

Testing for Fault Diversity in Reinforcement Learning

Quentin Mazouni, Helge Spieker, Arnaud Gotlieb +1

Reinforcement Learning is the premier technique to approach sequential decision problems, including complex tasks such as driving cars and landing spacecraft. Among the software va…

cs.SE2023★ 1 cited

A Review of Validation and Verification of Neural Network-based Policies for Sequential Decision Making

Q. Mazouni, H. Spieker, A. Gotlieb +1

In sequential decision making, neural networks (NNs) are nowadays commonly used to represent and learn the agent's policy. This area of application has implied new software quality…