Network Simulator-centric Compositional Testing
March 04, 2025 Β· Declared Dead Β· π Formal Techniques for (Networked and) Distributed Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tom Rousseaux, Christophe Crochet, John Aoga, Axel Legay
arXiv ID
2503.04810
Category
cs.SE: Software Engineering
Cross-listed
cs.CR,
cs.NI,
cs.SC
Citations
4
Venue
Formal Techniques for (Networked and) Distributed Systems
Last Checked
4 months ago
Abstract
This article introduces a novel methodology, Network Simulator-centric Compositional Testing (NSCT), to enhance the verification of network protocols with a particular focus on time-varying network properties. NSCT follows a Model-Based Testing (MBT) approach. These approaches usually struggle to test and represent time-varying network properties. NSCT also aims to achieve more accurate and reproducible protocol testing. It is implemented using the Ivy tool and the Shadow network simulator. This enables online debugging of real protocol implementations. A case study on an implementation of QUIC (picoquic) is presented, revealing an error in its compliance with a time-varying specification. This error has subsequently been rectified, highlighting NSCT's effectiveness in uncovering and addressing real-world protocol implementation issues. The article underscores NSCT's potential in advancing protocol testing methodologies, offering a notable contribution to the field of network protocol verification.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted