Tse-Hsun Chen
Concordia University
19 Papers
39 Citations
Tse-Hsun Chen is an academic researcher from Concordia University. The author has contributed to research in topics: Computer science & Source code. The author has an hindex of 8, co-authored 19 publications. Previous affiliations of Tse-Hsun Chen include Concordia University Wisconsin.
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Papers
Logram: Efficient Log Parsing Using n-Gram Dictionaries
TL;DR: Logram as mentioned in this paper leverages-gram dictionaries to achieve efficient log parsing and achieves a higher parsing accuracy than the best existing approaches (i.e., at least 10% higher, on average) and also outperforms these approaches in efficiency, achieving 1.8 to 5.1 times faster than the second-fastest approaches in terms of end-to-end parsing time.
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Dlfinder: characterizing and detecting duplicate logging code smells
Zhenhao Li,Tse-Hsun Chen,Jinqiu Yang,Weiyi Shang +3 more
- 25 May 2019
TL;DR: This paper manually studied over 3K duplicate logging statements and their surrounding code in four large-scale open source systems and integrated their results and developers' feedback into an automated static analysis tool, DLFinder, which automatically detects problematic duplicate logging code smells.
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A first look at the integration of machine learning models in complex autonomous driving systems: a case study on Apollo
Zi Peng,Jinqiu Yang,Tse-Hsun Chen,Lei Ma +3 more
- 08 Nov 2020
TL;DR: An in-depth case study on Apollo, which is one of the state-of-the-art ADS, widely adopted by major automakers worldwide, and reveals potential maintenance challenges of complex ML-powered systems and identifies future directions to improve the quality assurance of ADS and general ML systems.
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•Posted Content
Logram: Efficient Log Parsing Using n-Gram Dictionaries
TL;DR: An automated log parsing approach, Logram, which leverages n-gram dictionaries to achieve efficient log parsing, and it is demonstrated that Logram can support effective online parsing of logs, achieving similar parsing results and efficiency with the offline mode.
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The secret life of test smells - an empirical study on test smell evolution and maintenance
TL;DR: Wang et al. as discussed by the authors conducted an empirical study on 12 real-world open-source systems to study the evolution and maintenance of test smells, and how test smells are related to software quality.
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