Blockchain Enabled Smart Contract Based Applications: Deficiencies with the Software Development Life Cycle Models

• URL: http://arxiv.org/abs/2001.10589v1
• Date: Tue, 21 Jan 2020 03:48:46 GMT
• Title: Blockchain Enabled Smart Contract Based Applications: Deficiencies with the Software Development Life Cycle Models
• Authors: Mahdi H. Miraz and Maaruf Ali
• Abstract summary: The immutability of the blocks, where the smart contracts are stored, causes conflicts with the traditional Software Development Life Cycle (SDLC) models. This research article addresses this current problem by first exploring the six traditional SDLC models. It advocates that there is an urgent need to develop new standard model(s) to address the arising issues.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the recent popularity of Blockchain and other Distributed Ledger Technologies (DLT), blockchain enabled smart contract applications has attracted increased research focus. However, the immutability of the blocks, where the smart contracts are stored, causes conflicts with the traditional Software Development Life Cycle (SDLC) models usually followed by software engineers. This clearly shows the unsuitability of the application of SDLC in designing blockchain enabled smart contract based applications. This research article addresses this current problem by first exploring the six traditional SDLC models, clearly identifying the conflicts in a table with the application of smart contracts and advocates that there is an urgent need to develop new standard model(s) to address the arising issues. The concept of both block immutability and contract is introduced. This is further set in a historical context from legacy smart contracts and blockchain enabled smart contracts extending to the difference between "shallow smart contracts" and "deep smart contracts". To conclude, the traditional SDLC models are unsuitable for blockchain enabled smart contract-based applications.

Related papers

• BlockFound: Customized blockchain foundation model for anomaly detection [47.04595143348698]
  BlockFound is a customized foundation model for anomaly blockchain transaction detection. We introduce a series of customized designs to model the unique data structure of blockchain transactions. BlockFound is the only method that successfully detects anomalous transactions on Solana with high accuracy.
  arXiv  Detail & Related papers  (2024-10-05T05:11:34Z)
• Versioned Analysis of Software Quality Indicators and Self-admitted Technical Debt in Ethereum Smart Contracts with Ethstractor [2.052808596154225]
  This paper proposes Ethstractor, the first smart contract collection tool for gathering a dataset of versioned smart contracts. The collected dataset is then used to evaluate the reliability of code metrics as indicators of vulnerabilities in smart contracts.
  arXiv  Detail & Related papers  (2024-07-22T18:27:29Z)
• Dual-view Aware Smart Contract Vulnerability Detection for Ethereum [5.002702845720439]
  We propose a Dual-view Aware Smart Contract Vulnerability Detection Framework named DVDet. The framework initially converts the source code and bytecode of smart contracts into weighted graphs and control flow sequences. Comprehensive experiments on the dataset show that our method outperforms others in detecting vulnerabilities.
  arXiv  Detail & Related papers  (2024-06-29T06:47:51Z)
• Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study [73.87110604150315]
  Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
  arXiv  Detail & Related papers  (2024-01-28T10:46:17Z)
• Graph Neural Networks Enhanced Smart Contract Vulnerability Detection of Educational Blockchain [4.239144309557045]
  This paper proposes a graph neural network based vulnerability detection for smart contracts in educational blockchains. The experimental results show that the proposed method is effective for the vulnerability detection of smart contracts.
  arXiv  Detail & Related papers  (2023-03-08T09:58:58Z)
• A formal model for ledger management systems based on contracts and temporal logic [0.0]
  In second-generation blockchains such as the ledger is coupled with smart contracts. The current implementation of smart contracts as arbitrary programming constructs has made them susceptible to dangerous bugs. We propose here to recompose the split and recover the reliability of databases by formalizing a notion of contract modelled as a finite-state automaton.
  arXiv  Detail & Related papers  (2021-09-30T15:34:28Z)
• Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion [48.744359070088166]
  Conventional smart contract vulnerability detection methods heavily rely on fixed expert rules. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. We develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features.
  arXiv  Detail & Related papers  (2021-06-17T07:12:13Z)
• Quantum-resistance in blockchain networks [46.63333997460008]
  This paper describes the work carried out by the Inter-American Development Bank, the IDB Lab, LACChain, Quantum Computing (CQC), and Tecnologico de Monterrey to identify and eliminate quantum threats in blockchain networks. The advent of quantum computing threatens internet protocols and blockchain networks because they utilize non-quantum resistant cryptographic algorithms.
  arXiv  Detail & Related papers  (2021-06-11T23:39:25Z)
• A Bytecode-based Approach for Smart Contract Classification [10.483992071557195]
  The number of smart contracts deployed on blockchain platforms is growing exponentially, which makes it difficult for users to find desired services by manual screening. Current research on smart contract classification focuses on Natural Language Processing (NLP) solutions which are based on contract source code. This paper proposes a classification model based on features from contract bytecode instead of source code to solve these problems.
  arXiv  Detail & Related papers  (2021-05-31T03:00:29Z)
• ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning [80.85273827468063]
  Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable. We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts. We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
  arXiv  Detail & Related papers  (2021-03-23T15:04:44Z)
• Quantum Multi-Solution Bernoulli Search with Applications to Bitcoin's Post-Quantum Security [67.06003361150228]
  A proof of work (PoW) is an important cryptographic construct enabling a party to convince others that they invested some effort in solving a computational task. In this work, we examine the hardness of finding such chain of PoWs against quantum strategies. We prove that the chain of PoWs problem reduces to a problem we call multi-solution Bernoulli search, for which we establish its quantum query complexity.
  arXiv  Detail & Related papers  (2020-12-30T18:03:56Z)

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.