Y
Yun Lin
Researcher at National University of Singapore
Publications - 27
Citations - 817
Yun Lin is an academic researcher from National University of Singapore. The author has contributed to research in topics: Software bug & Code refactoring. The author has an hindex of 10, co-authored 27 publications receiving 412 citations. Previous affiliations of Yun Lin include George Mason University & Fudan University.
Papers
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Journal ArticleDOI
Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues
TL;DR: An overview of real cyber attack incidents in traditional energy networks and those targeting the smart metering network is shown and a threat taxonomy is presented considering: 1) threats in system-level security; 2) threats and/or theft of services; and 3) threats to privacy.
Proceedings ArticleDOI
sFuzz: an efficient adaptive fuzzer for solidity smart contracts
TL;DR: SFuzz as discussed by the authors combines the strategy in the AFL fuzzer and an efficient lightweight multi-objective adaptive strategy targeting those hard-to-cover branches, and has been applied to more than 4 thousand smart contracts.
Proceedings ArticleDOI
Interactive and guided architectural refactoring with search-based recommendation
TL;DR: This paper presents Refactoring Navigator: a tool-supported and interactive recommendation approach for aiding architectural refactoring, which takes a given implementation as the starting point, a desired high-level design as the target, and iteratively recommends a series of refactored steps.
Proceedings ArticleDOI
Towards optimal concolic testing
TL;DR: A greedy algorithm is designed for approximating the optimal concolic testing strategy based on the probability of program paths and the cost of constraint solving and the results show that existing heuristics have much room to improve and the greedy algorithm often outperforms existingHeuristics.
Proceedings ArticleDOI
Detecting differences across multiple instances of code clones
TL;DR: The study shows that the approach to automatically detecting differences across multiple clone instances can significantly improve developers’performance inRefactoring decisions, refactoring details, and task completion time on clone-related refactored tasks.