Formal Specification and Verification of Solidity Contracts with Events
May 20, 2020 Β· Declared Dead Β· π FMBC@CAV
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Γkos Hajdu, Dejan JovanoviΔ, Gabriela Ciocarlie
arXiv ID
2005.10382
Category
cs.PL: Programming Languages
Cross-listed
cs.LO,
cs.SE
Citations
8
Venue
FMBC@CAV
Last Checked
3 months ago
Abstract
Events in the Solidity language provide a means of communication between the on-chain services of decentralized applications and the users of those services. Events are commonly used as an abstraction of contract execution that is relevant from the users' perspective. Users must, therefore, be able to understand the meaning and trust the validity of the emitted events. This paper presents a source-level approach for the formal specification and verification of Solidity contracts with the primary focus on events. Our approach allows specification of events in terms of the on-chain data that they track, and predicates that define the correspondence between the blockchain state and the abstract view provided by the events. The approach is implemented in solc-verify, a modular verifier for Solidity, and we demonstrate its applicability with various examples.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
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