CATMA: Conformance Analysis Tool For Microservice Applications
January 18, 2024 Β· Declared Dead Β· π 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Clinton Cao, Simon Schneider, NicolΓ‘s E. DΓaz Ferreyra, Sicco Verwer, Annibale Panichella, Riccardo Scandariato
arXiv ID
2401.09838
Category
cs.SE: Software Engineering
Citations
3
Venue
2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
Last Checked
4 months ago
Abstract
The microservice architecture allows developers to divide the core functionality of their software system into multiple smaller services. However, this architectural style also makes it harder for them to debug and assess whether the system's deployment conforms to its implementation. We present CATMA, an automated tool that detects non-conformances between the system's deployment and implementation. It automatically visualizes and generates potential interpretations for the detected discrepancies. Our evaluation of CATMA shows promising results in terms of performance and providing useful insights. CATMA is available at \url{https://cyber-analytics.nl/catma.github.io/}, and a demonstration video is available at \url{https://youtu.be/WKP1hG-TDKc}.
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