Compositional Formal Analysis Based on Conventional Engineering Models
April 07, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tyler D. Smith, Ryan Peroutka, Robert Edman
arXiv ID
2004.03666
Category
cs.SE: Software Engineering
Cross-listed
eess.SY
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Applications of formal methods for state space exploration have been successfully applied to evaluate robust critical software systems. Formal methods enable discovery of error conditions that conventional testing may miss, and can aid in planning complex system operations. However, broad application of formal methods has been hampered by the effort required to generate formal specifications for real systems. In this paper we present State Linked Interface Compliance Engine for Data (SLICED), a methodology that addresses the complexity of formal state machine specification generation by leveraging conventional engineering models to derive compositional formal state models and to generate formal assertions on the state machines. We demonstrate SLICED using the Virtual ADAPT model published by NASA and validate our results by replicating them using Simulink.
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