History analysis of decentralized applications

In recent years, the popularity of blockchain has been on the rise due to the increasing usage of blockchain technologies such as cryptocurrencies, smart contracts, and other types of decentralised applications. The global blockchain market size has been expected to grow from USD 3.0 billion in 2020...

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Bibliographic Details
Main Author: Goh, Damien Hong Jun
Other Authors: Li Yi
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/157302
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Institution: Nanyang Technological University
Language: English
Description
Summary:In recent years, the popularity of blockchain has been on the rise due to the increasing usage of blockchain technologies such as cryptocurrencies, smart contracts, and other types of decentralised applications. The global blockchain market size has been expected to grow from USD 3.0 billion in 2020 to USD 39.7 billion by 2025 [1]. In this project, the purpose was to come up with data analysis techniques together with the use of existing program analysis tools to focus on extracting ERC-20 tokens and Ether transaction data for analysis to solve practical problems in analysing smart contracts. Ethereum, a decentralized smart contracts platform, was the platform used to gather the transaction data for the smart contracts. Different types of transaction data from users were first gathered by getting their account addresses and extracted through different blockchain APIs (Application Programming Interface). Once all the relevant transaction data was collected, the data for a user was merged based on certain conditions to give a more detailed and complete look of the transaction history for the user. From the resulting data, different types of graphs and diagrams were created for the analysis to help study and understand the data better. This allowed for different insights and information such as trends and interesting conclusions to be drawn from the smart contracts used and the transaction data coming from different users.