Neurosymbolic Architectural Reasoning: Towards Formal Analysis through Neural Software Architecture Inference
March 20, 2025 Β· Declared Dead Β· π 2025 IEEE/ACM 1st International Workshop on Neuro-Symbolic Software Engineering (NSE)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Steffen Herbold, Christoph Knieke, Andreas Rausch, Christian Schindler
arXiv ID
2503.16262
Category
cs.SE: Software Engineering
Citations
2
Venue
2025 IEEE/ACM 1st International Workshop on Neuro-Symbolic Software Engineering (NSE)
Last Checked
4 months ago
Abstract
Formal analysis to ensure adherence of software to defined architectural constraints is not yet broadly used within software development, due to the effort involved in defining formal architecture models. Within this paper, we outline neural architecture inference to solve the problem of having a formal architecture definition for subsequent symbolic reasoning over these architectures, enabling neurosymbolic architectural reasoning. We discuss how this approach works in general and outline a research agenda based on six general research question that need to be addressed, to achieve this vision.
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