VLDB 2026 Research / reviewers in the wild / expert
Kanchanok Kannee
dblp:326/5123
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 1 first-author · 2 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Intertwining Communities: Exploring Libraries that Cross Software EcosystemsabstractUsing libraries in applications has helped developers reduce the costs of reinventing already existing code. However, an increase in diverse technology stacks and third-party library usage has led developers to inevitably switch technologies and search for similar libraries implemented in the new technology. To assist with searching for these replacement libraries, maintainers have started to release their libraries to multiple ecosystems. Our goal is to explore the extent to which these libraries are intertwined between ecosystems. We perform a large-scale empirical study of 1.1 million libraries from five different software ecosystems, i.e., PyPI, CRAN, Maven, RubyGems, and NPM, to identify 4,146 GitHub repositories. As a starting point, insights from the study raise implications for library maintainers, users, contributors, and researchers into understanding how these different ecosystems are becoming more intertwined with each other. Kanchanok Kannee, Raula Gaikovina Kula, Supatsara Wattanakriengkrai, Ken-ichi Matsumoto |
MSR | 1 |
| 2022 | Visualizing Contributor Code Competency for PyPI Libraries: Preliminary ResultsabstractPython is known to be used by beginners to professional programmers. Python provides functionality to its community of users through PyPI libraries, which allows developers to reuse functionalities to an application. However, it is unknown the extent to which these PyPI libraries require proficient code in their implementation. We conjecture that PyPI contributors may decide to implement more advanced Pythonic code, or stick with more basic Python code. Are complex codes only committed by few contributors, or only to specific files? The new idea in this paper is to confirm who and where complex code is implemented. Hence, we present a visualization to show the relationship between proficient code, contributors, and files. Analyzing four PyPI projects, we are able to explore which files contain more elegant code, and which contributors committed to these files. Our results show that most files contain more basic competency files, and that not every contributor contributes competent code. We show how our visualization is able to summarize such information, and opens up different possibilities for understanding how to make elegant contributions. Indira Febriyanti, Raula Gaikovina Kula, Ruksit Rojpaisarnkit, Kanchanok Kannee, Yusuf Sulistyo Nugroho, Ken-ichi Matsumoto |
APSEC | 4 |