Demo Data from VLDB 2026 / reviewers in the wild / expert
Zhiming Peng
dblp:68/8545
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution
code clone detection |
0.2 | 1 | 2016 | Mining revision histories to detect cross-language clones without intermediates · ASE 2016 |
Software maintenance and evolution › code clone detection
cross-language clone detection |
0.2 | 1 | 2016 | Mining revision histories to detect cross-language clones without intermediates · ASE 2016 |
Empirical software engineering
mining software repositories |
0.2 | 1 | 2016 | Mining revision histories to detect cross-language clones without intermediates · ASE 2016 |
Machine learning › Learning theory
generalization bounds |
0.2 | 1 | 2013 | 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 |
0.2 | 1 | 2013 | 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 |
0.2 | 1 | 2013 | The learning performance of support vector machine classification based on Markov sampling · Sci. China Inf. Sci. 2013 |
Machine learning › Learning theory
statistical learning theory |
0.2 | 1 | 2013 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Mining revision histories to detect cross-language clones without intermediatesabstractTo 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 |
ASE | 2 |
| 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 |