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Demo Data from EDBT 2026 / reviewers in the wild / expert

Micheline Bénédicte Moumoula

dblp:385/1419 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2026
0009-0005-1206-2270ORCID · corroborated

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

Software engineering, systems software and programming languages · 2 · 2 first-author · 2 since 2021

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.

Software engineering, system software, and programming languages
1 paper
Software maintenance and evolution · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software maintenance and evolution
code clone detection		 0.812024
Cross-lingual Code Clone Detection: When LLMs Fail Short Against Embedding-based Classifier · ASE 2024
Software maintenance and evolution › code clone detection
cross-language clone detection		 0.812024
Cross-lingual Code Clone Detection: When LLMs Fail Short Against Embedding-based Classifier · ASE 2024
Software maintenance and evolution
code reuse		 0.212024
Cross-lingual Code Clone Detection: When LLMs Fail Short Against Embedding-based Classifier · ASE 2024

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

large language model · 0.8embedding-based classifier · 0.8
YearPublicationVenuePosition
2026 Programming Language Confusion: When Code LLMs Can't Keep Their Languages Straight
Micheline Bénédicte Moumoula, Serge Lionel Nikiema, Abdoul Kader Kaboré, Jacques Klein, Tegawendé F. Bissyandé
 SANER1
2024 Cross-lingual Code Clone Detection: When LLMs Fail Short Against Embedding-based Classifier
abstract
Cross-lingual code clone detection has gained attention in software development due to the use of multiple programming languages. Recent advances in machine learning, particularly Large Language Models (LLMs), have motivated a reexamination of this problem.
Micheline Bénédicte Moumoula, Abdoul Kader Kaboré, Jacques Klein, Tegawendé F. Bissyandé
 ASE1