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Full-Text Articles in Databases and Information Systems
Practitioners' Expectations On Automated Code Comment Generation, Xing Hu, Xin Xia, David Lo, Zhiyuan Wan, Qiuyuan Chen, Thomas Zimmermann
Practitioners' Expectations On Automated Code Comment Generation, Xing Hu, Xin Xia, David Lo, Zhiyuan Wan, Qiuyuan Chen, Thomas Zimmermann
Research Collection School Of Computing and Information Systems
Good comments are invaluable assets to software projects, as they help developers understand and maintain projects. However, due to some poor commenting practices, comments are often missing or inconsistent with the source code. Software engineering practitioners often spend a significant amount of time and effort reading and understanding programs without or with poor comments. To counter this, researchers have proposed various techniques to automatically generate code comments in recent years, which can not only save developers time writing comments but also help them better understand existing software projects. However, it is unclear whether these techniques can alleviate comment issues and …
Why Do Smart Contracts Self-Destruct? Investigating The Selfdestruct Function On Ethereum, Jiachi Chen, Xin Xia, David Lo, John C. Grundy
Why Do Smart Contracts Self-Destruct? Investigating The Selfdestruct Function On Ethereum, Jiachi Chen, Xin Xia, David Lo, John C. Grundy
Research Collection School Of Computing and Information Systems
The selfdestruct function is provided by Ethereum smart contracts to destroy a contract on the blockchain system. However, it is a double-edged sword for developers. On the one hand, using the selfdestruct function enables developers to remove smart contracts (SCs) from Ethereum and transfers Ethers when emergency situations happen, e.g., being attacked. On the other hand, this function can increase the complexity for the development and open an attack vector for attackers. To better understand the reasons why SC developers include or exclude the selfdestruct function in their contracts, we conducted an online survey to collect feedback from them and …
Smart Contract Security: A Practitioners' Perspective, Zhiyuan Wan, Xin Xia, David Lo, Jiachi Chen, Xiapu Luo, Xiaohu Yang
Smart Contract Security: A Practitioners' Perspective, Zhiyuan Wan, Xin Xia, David Lo, Jiachi Chen, Xiapu Luo, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Smart contracts have been plagued by security incidents, which resulted in substantial financial losses. Given numerous research efforts in addressing the security issues of smart contracts, we wondered how software practitioners build security into smart contracts in practice. We performed a mixture of qualitative and quantitative studies with 13 interviewees and 156 survey respondents from 35 countries across six continents to understand practitioners' perceptions and practices on smart contract security. Our study uncovers practitioners' motivations and deterrents of smart contract security, as well as how security efforts and strategies fit into the development lifecycle. We also find that blockchain platforms …
Do Users Care About Ad's Performance Costs? Exploring The Effects Of The Performance Costs Of In-App Ads On User Experience, Cuiyun Gao, Jichuan Zeng, Federica Sarro, David Lo, Irwin King, Michael R. Lyu
Do Users Care About Ad's Performance Costs? Exploring The Effects Of The Performance Costs Of In-App Ads On User Experience, Cuiyun Gao, Jichuan Zeng, Federica Sarro, David Lo, Irwin King, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Context: In-app advertising is the primary source of revenue for many mobile apps. The cost of advertising (ad cost) is non-negligible for app developers to ensure a good user experience and continuous profits. Previous studies mainly focus on addressing the hidden performance costs generated by ads, including consumption of memory, CPU, data traffic, and battery. However, there is no research on analyzing users’ perceptions of ads’ performance costs to our knowledge.Objective: To fill this gap and better understand the effects of performance costs of in-app ads on user experience, we conduct a study on analyzing user concerns about ads’ performance …
An Exploratory Study On The Introduction And Removal Of Different Types Of Technical Debt In Deep Learning Frameworks, Jiakun Liu, Qiao Huang, Xin Xia, Emad Shihab, David Lo, Shanping Li
An Exploratory Study On The Introduction And Removal Of Different Types Of Technical Debt In Deep Learning Frameworks, Jiakun Liu, Qiao Huang, Xin Xia, Emad Shihab, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
To complete tasks faster, developers often have to sacrifice the quality of the software. Such compromised practice results in the increasing burden to developers in future development. The metaphor, technical debt, describes such practice. Prior research has illustrated the negative impact of technical debt, and many researchers investigated how developers deal with a certain type of technical debt. However, few studies focused on the removal of different types of technical debt in practice. To fill this gap, we use the introduction and removal of different types of self-admitted technical debt (i.e., SATD) in 7 deep learning frameworks as an example. …
An Empirical Study Of The Dependency Networks Of Deep Learning Libraries, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia
An Empirical Study Of The Dependency Networks Of Deep Learning Libraries, Junxiao Han, Shuiguang Deng, David Lo, Chen Zhi, Jianwei Yin, Xin Xia
Research Collection School Of Computing and Information Systems
Deep Learning techniques have been prevalent in various domains, and more and more open source projects in GitHub rely on deep learning libraries to implement their algorithms. To that end, they should always keep pace with the latest versions of deep learning libraries to make the best use of deep learning libraries. Aptly managing the versions of deep learning libraries can help projects avoid crashes or security issues caused by deep learning libraries. Unfortunately, very few studies have been done on the dependency networks of deep learning libraries. In this paper, we take the first step to perform an exploratory …