The Myth of Immutability: A Multivocal Review on Smart Contract Upgradeability
- URL: http://arxiv.org/abs/2504.02719v1
- Date: Thu, 03 Apr 2025 16:02:46 GMT
- Title: The Myth of Immutability: A Multivocal Review on Smart Contract Upgradeability
- Authors: Ilham Qasse, Isra M. Ali, Nafisa Ahmed, Mohammad Hamdaqa, Björn Þór Jónsson,
- Abstract summary: immutability of smart contracts on blockchain platforms like promotes security and trustworthiness but presents challenges for updates, bug fixes, or adding new features post-deployment.<n>Despite various upgrade mechanisms proposed in academic research and industry, a comprehensive analysis of their trade-offs and practical implications is lacking.<n>This study aims to systematically identify, classify, and evaluate existing smart contract upgrade mechanisms.
- Score: 0.6124448703245243
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The immutability of smart contracts on blockchain platforms like Ethereum promotes security and trustworthiness but presents challenges for updates, bug fixes, or adding new features post-deployment. These limitations can lead to vulnerabilities and outdated functionality, impeding the evolution and maintenance of decentralized applications. Despite various upgrade mechanisms proposed in academic research and industry, a comprehensive analysis of their trade-offs and practical implications is lacking. This study aims to systematically identify, classify, and evaluate existing smart contract upgrade mechanisms, bridging the gap between theoretical concepts and practical implementations. It introduces standardized terminology and evaluates the trade-offs of different approaches using software quality attributes. We conducted a Multivocal Literature Review (MLR) to analyze upgrade mechanisms from both academic research and industry practice. We first establish a unified definition of smart contract upgradeability and identify core components essential for understanding the upgrade process. Based on this definition, we classify existing methods into full upgrade and partial upgrade approaches, introducing standardized terminology to harmonize the diverse terms used in the literature. We then characterize each approach and assess its benefits and limitations using software quality attributes such as complexity, flexibility, security, and usability. The analysis highlights significant trade-offs among upgrade mechanisms, providing valuable insights into the benefits and limitations of each approach. These findings guide developers and researchers in selecting mechanisms tailored to specific project requirements.
Related papers
- A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making.
With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems.
We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
arXiv Detail & Related papers (2025-04-12T01:27:49Z) - Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview [3.135279672650891]
This paper focuses on state-of-the-art software testing and verification.<n>It focuses on three key approaches: classical formal methods, LLM-based analysis, and emerging hybrid techniques.<n>We analyze whether integrating formal rigor with LLM-driven insights can enhance the effectiveness and scalability of software verification.
arXiv Detail & Related papers (2025-03-13T18:22:22Z) - Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis [0.0]
We analyze the state of the art in machine-learning vulnerability detection for smart contracts.
We discuss best practices to enhance the accuracy, scope, and efficiency of vulnerability detection in smart contracts.
arXiv Detail & Related papers (2024-07-26T10:09:44Z) - Benchmarks as Microscopes: A Call for Model Metrology [76.64402390208576]
Modern language models (LMs) pose a new challenge in capability assessment.
To be confident in our metrics, we need a new discipline of model metrology.
arXiv Detail & Related papers (2024-07-22T17:52:12Z) - Vulnerability Detection in Smart Contracts: A Comprehensive Survey [10.076412566428756]
This study examines the potential of machine learning techniques to improve the detection and mitigation of vulnerabilities in smart contracts.
We analysed 88 articles published between 2018 and 2023 from the following databases: IEEE, ACM, ScienceDirect, Scopus, and Google Scholar.
The findings reveal that classical machine learning techniques, including KNN, RF, DT, XG-Boost, and SVM, outperform static tools in vulnerability detection.
arXiv Detail & Related papers (2024-07-08T11:51:15Z) - Immutable in Principle, Upgradeable by Design: Exploratory Study of Smart Contract Upgradeability [0.717789756063617]
This study identifies upgradeable contracts and examines their upgrade history to uncover trends, preferences, and challenges associated with modifications.
The evidence from analyzing over 44 million contracts shows that only 3% have upgradeable characteristics, with only 0.34% undergoing upgrades.
The relationship between upgrades and user activity is complex, suggesting that additional factors significantly affect the use of smart contracts beyond their evolution.
arXiv Detail & Related papers (2024-07-01T17:35:37Z) - Coding for Intelligence from the Perspective of Category [66.14012258680992]
Coding targets compressing and reconstructing data, and intelligence.
Recent trends demonstrate the potential homogeneity of these two fields.
We propose a novel problem of Coding for Intelligence from the category theory view.
arXiv Detail & Related papers (2024-07-01T07:05:44Z) - What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks [16.795332276080888]
We propose a fine-grained validation framework for explainable artificial intelligence systems.
We recognise their inherent modular structure: technical building blocks, user-facing explanatory artefacts and social communication protocols.
arXiv Detail & Related papers (2024-03-19T13:45:34Z) - MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation [60.65820977963331]
We introduce a novel evaluation paradigm for Large Language Models (LLMs)
This paradigm shifts the emphasis from result-oriented assessments, which often neglect the reasoning process, to a more comprehensive evaluation.
By applying this paradigm in the GSM8K dataset, we have developed the MR-GSM8K benchmark.
arXiv Detail & Related papers (2023-12-28T15:49:43Z) - MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration [98.18244218156492]
Large Language Models (LLMs) have significantly advanced natural language processing.<n>As their applications expand into multi-agent environments, there arises a need for a comprehensive evaluation framework.<n>This work introduces a novel competition-based benchmark framework to assess LLMs within multi-agent settings.
arXiv Detail & Related papers (2023-11-14T21:46:27Z) - A Unifying Perspective on Multi-Calibration: Game Dynamics for
Multi-Objective Learning [63.20009081099896]
We provide a unifying framework for the design and analysis of multicalibrated predictors.
We exploit connections to game dynamics to achieve state-of-the-art guarantees for a diverse set of multicalibration learning problems.
arXiv Detail & Related papers (2023-02-21T18:24:17Z) - Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach [123.55983746427572]
We propose novel learning algorithms to recover the dynamic Vickrey-Clarke-Grove (VCG) mechanism over multiple rounds of interaction.
A key contribution of our approach is incorporating reward-free online Reinforcement Learning (RL) to aid exploration over a rich policy space.
arXiv Detail & Related papers (2022-02-25T16:17:23Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z)
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.