Abstract. Blockchain technology supports digital assets, which can take the form of cryptocurrencies and tokens. Tokens are usually created on top of the blockchain platform, using smart contracts. Two main categories of tokens exist: Fungible Tokens and Non-Fungible Tokens (NFTs). Here, we focus on NFTs and propose a correlation analysis between various NFTs’ characteristics and the popularity of the NFTs. The results can have practical implications for both designers and users.
Abstract. Blockchain has been one of Gartner’s Top 10 Strategic Technology Trends for several consecutive years. The technology has evolved from a platform allowing transactions of cryptocurrency between peers (e.g. Bitcoin) to a platform allowing the design of Decentralized Applications (DApps). Despite their growing popularity, little attention has been paid to the Software Engineering aspect of DApps. In this work, we aim to start bridging this gap by addressing the Requirements Engineering of DApps. We collect, analyze and integrate DApp user reviews in order to propose a first list of user requirements for DApps. The results can have practical implications for both practitioners and researchers. The former can use the results to guide them in the design of DApps, while the latter can see this paper as a first result to build upon to advance the software engineering field of blockchain-based applications.
The 2nd Conference on Blockchain Research & Applications for Innovative Networks and Services took place in September 2020 (BRAINS2020) was supposed to be held in Paris, but unfortunately due to the COVID-2019 pandemic, the conference had to be held virtually.
In my very first poster session, I’ve presented the poster you can see above which focuses on the prediction of gas for transactions on Ethereum. Obviously, the conditions were not ideal for exchanges with the other attendees, but the organizers did a great job in enabling interactions despite the virtual setting.
Abstract. The author uses data about transactions onEthereum as sources for studying the relationship between thehistoric of transactions for a given address and the amountof gas consumed for a transaction. The author combines dataabout transactions, and blocks to predict the gas usage for atransaction. Specifically, how much gas will be consumed for the next transaction, given the initiator’s transaction history. The results demonstrate the value of considering the transactionhistory for gas usage predictions.
Feel free to leave any comment or question you might have regarding this poster in itself or the topic of the poster.
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