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20142023
most citedMETTLE: a METamorphic testing approach to assessing and validating unsupervised machine LEarning systems

62 citations · 195 across the 10 of their papers we have counts for

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Showing cs.SEShow all

6 papers · 1 filter

cs.SE2022

Clone Detection on Large Scala Codebases

Wahidur Rahman, Yisen Xu, Fan Pu +7

Code clones are identical or similar code segments. The wide existence of code clones can increase the cost of maintenance and jeopardise the quality of software. The research comm…

cs.SE2021

PyART: Python API Recommendation in Real-Time

Xincheng He, Lei Xu, Xiangyu Zhang +3

API recommendation in real-time is challenging for dynamic languages like Python. Many existing API recommendation techniques are highly effective, but they mainly support static l…

cs.SE2020

Prioritizing documentation effort: Can we do better?

Shiran Liu, Zhaoqiang Guo, Yanhui Li +5

Code documentations are essential for software quality assurance, but due to time or economic pressures, code developers are often unable to write documents for all modules in a pr…

cs.SE2019★ 7 cited

MAT: A simple yet strong baseline for identifying self-admitted technical debt

Zhaoqiang Guo, Shiran Liu, Jinping Liu +5

In the process of software evolution, developers often sacrifice the long-term code quality to satisfy the short-term goals due to specific reasons, which is called technical debt.…

cs.SE2018★ 62 cited

METTLE: a METamorphic testing approach to assessing and validating unsupervised machine LEarning systems

Xiaoyuan Xie, Zhiyi Zhang, Tsong Yueh Chen +3

Unsupervised machine learning is the training of an artificial intelligence system using information that is neither classified nor labeled, with a view to modeling the underlying…

cs.SE2017★ 7 cited

Connecting Software Metrics across Versions to Predict Defects

Yibin Liu, Yanhui Li, Jianbo Guo +2

Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort…