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Zhiming Peng

dblp:68/8545 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Probabilistic and Bayesian machine learning · 40% Learning theory · 40% Optimization for machine learning · 20%
Software engineering, system software, and programming languages
1 paper
Software maintenance and evolution · 67% Empirical software engineering · 33%

Topics — the 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software maintenance and evolution
code clone detection
0.212016
Mining revision histories to detect cross-language clones without intermediates · ASE 2016
Software maintenance and evolution › code clone detection
cross-language clone detection
0.212016
Mining revision histories to detect cross-language clones without intermediates · ASE 2016
Empirical software engineering
mining software repositories
0.212016
Mining revision histories to detect cross-language clones without intermediates · ASE 2016
Machine learning › Learning theory
generalization bounds
0.212013
The learning performance of support vector machine classification based on Markov sampling · Sci. China Inf. Sci. 2013
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › regression › generalized linear model › logistic regression
kernel logistic regression
0.212013
Sparse kernel logistic regression based on L 1/2 regularization · Sci. China Inf. Sci. 2013
Machine learning › Probabilistic and Bayesian machine learning › monte carlo methods
markovian sampling
0.212013
The learning performance of support vector machine classification based on Markov sampling · Sci. China Inf. Sci. 2013
Machine learning › Learning theory
statistical learning theory
0.212013
The learning performance of support vector machine classification based on Markov sampling · Sci. China Inf. Sci. 2013

Methods — techniques the papers use, named apart from their topics

revision history mining · 0.2support vector machine · 0.2markov sampling · 0.2
YearPublicationVenuePosition
2016 Mining revision histories to detect cross-language clones without intermediates
abstract
To attract more users on different platforms, many projects release their versions in multiple programming languages (e.g., Java and C#). They typically have many code snippets that implement similar functionalities, i.e., cross-language clones. Programmers often need to track and modify cross-language clones consistently to maintain similar functionalities across different language implementations. In literature, researchers have proposed approaches to detect cross- language clones, mostly for languages that share a common intermediate language (such as the .NET language family) so that techniques for detecting single-language clones can be applied. As a result, those approaches cannot detect cross-language clones for many projects that are not implemented in a .NET language. To overcome the limitation, in this paper, we propose a novel approach, CLCMiner, that detects cross-language clones automatically without the need of an intermediate language. Our approach mines such clones from revision histories, which reflect how programmers maintain cross-language clones in practice. We have implemented a prototype tool for our approach and conducted an evaluation on five open source projects that have versions in Java and C#. The results show that CLCMiner achieves high accuracy and point to promising future work.
Xiao Cheng 0006, Zhiming Peng, Lingxiao Jiang, Hao Zhong 0001, Haibo Yu 0001, Jianjun Zhao 0001
ASE2
2013 Sparse kernel logistic regression based on L 1/2 regularization
Zhiming Peng, Wenfeng Jing
Sci. China Inf. Sci.2
2013 The learning performance of support vector machine classification based on Markov sampling
Bin Zou 0002, Zhiming Peng, Zongben Xu
Sci. China Inf. Sci.2