CLawK: Monitoring Business Processes in Smart Contracts

• URL: http://arxiv.org/abs/2305.08254v1
• Date: Sun, 14 May 2023 21:33:19 GMT
• Title: CLawK: Monitoring Business Processes in Smart Contracts
• Authors: Mojtaba Eshghie, Wolfgang Ahrendt, Cyrille Artho, Thomas Troels Hildebrandt, Gerardo Schneider
• Abstract summary: We present CLawK, a runtime monitoring tool that leverages business process specifications written in DCR graphs to provide runtime verification of smart contract execution. We demonstrate how CLawK can detect and flag deviations from specified behaviors in smart contracts deployed in the network without code instrumentation and any additional gas costs.
• Score: 2.3709422532220805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Smart contracts embody complex business processes that can be difficult to analyze statically. In this paper, we present CLawK, a runtime monitoring tool that leverages business process specifications written in DCR graphs to provide runtime verification of smart contract execution. We demonstrate how CLawK can detect and flag deviations from specified behaviors in smart contracts deployed in the Ethereum network without code instrumentation and any additional gas costs.

Related papers

• Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
  We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework. Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
  arXiv  Detail & Related papers  (2024-10-02T09:11:10Z)
• Contractual Reinforcement Learning: Pulling Arms with Invisible Hands [68.77645200579181]
  We propose a theoretical framework for aligning economic interests of different stakeholders in the online learning problems through contract design. For the planning problem, we design an efficient dynamic programming algorithm to determine the optimal contracts against the far-sighted agent. For the learning problem, we introduce a generic design of no-regret learning algorithms to untangle the challenges from robust design of contracts to the balance of exploration and exploitation.
  arXiv  Detail & Related papers  (2024-07-01T16:53:00Z)
• Solvent: liquidity verification of smart contracts [2.680854115314008]
  A current limitation of smart contract verification tools is that they are not really effective in expressing and verifying liquidity properties regarding the exchange of crypto-assets. We propose solvent, a tool aimed at verifying these kinds of properties, which are beyond the reach of existing verification tools for Solidity.
  arXiv  Detail & Related papers  (2024-04-27T10:54:50Z)
• Specification Mining for Smart Contracts with Trace Slicing and Predicate Abstraction [10.723903783651537]
  We propose a specification mining approach to infer contract specifications from past transactionhistories. Our approach derives high-level behavioral automata of function invocations, accompanied byprogram invariants statistically inferred from the transaction histories.
  arXiv  Detail & Related papers  (2024-03-20T03:39:51Z)
• Formally Verifying a Real World Smart Contract [52.30656867727018]
  We search for a tool capable of formally verifying a real-world smart contract written in a recent version of Solidity. In this article, we present our search for a tool capable of formally verifying a real-world smart contract written in a recent version of Solidity.
  arXiv  Detail & Related papers  (2023-07-05T14:30:21Z)
• Capturing Smart Contract Design with DCR Graphs [2.3709422532220805]
  We argue that DCR graphs are a suitable formalization tool for smart contracts because they explicitly and visually capture the mentioned features. Applying these patterns shows that DCR graphs facilitate the development and analysis of correct and reliable smart contracts.
  arXiv  Detail & Related papers  (2023-05-08T09:44:57Z)
• Safety Verification of Declarative Smart Contracts [4.303272418564008]
  This paper presents an automated safety verification tool, DCV, that targets declarative smart contracts written in DeCon. Our evaluation on 20 benchmark contracts shows that DCV is effective in verifying smart contracts adapted from public repositories, and can verify contracts not supported by other tools.
  arXiv  Detail & Related papers  (2022-11-26T15:02:37Z)
• ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction [84.0634340572349]
  We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts. In this work, we first comprehensively analyze the complexity issues of contracts and distill out three implicit relations commonly found in contracts. We propose a novel framework ConReader to exploit the above three relations for better contract understanding and improving CCE.
  arXiv  Detail & Related papers  (2022-10-17T02:15:18Z)
• Detecting Logical Relation In Contract Clauses [94.85352502638081]
  We develop an approach to automate the extraction of logical relations between clauses in a contract. The resulting approach should help contract authors detecting potential logical conflicts between clauses.
  arXiv  Detail & Related papers  (2021-11-02T19:26:32Z)
• CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT (Extended Version) [62.96267257163426]
  We introduce the CoCoMoT (Computing Conformance Modulo Theories) framework. First, we show how SAT-based encodings studied in the pure control-flow setting can be lifted to our data-aware case. Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering.
  arXiv  Detail & Related papers  (2021-03-18T20:22:50Z)
• Profiling Gas Consumption in Solidity Smart Contracts [3.0378875015087567]
  We propose GasMet, a suite of metrics for statically evaluating the code quality of a smart contract from the gas consumption perspective. An experiment involving 2,186 smart contracts demonstrates that the proposed metrics have direct associations with deployment costs.
  arXiv  Detail & Related papers  (2020-08-12T17:26:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.