Jakub Zitny

dblp:207/6545 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

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

Software 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.

Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 33% Compilers and program optimization · 33% Software maintenance and evolution · 33%

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

TopicWeightPapersLastEvidence papers
Compilers and program optimization
code duplication
0.312017
DéjàVu: a map of code duplicates on GitHub · Proc. ACM Program. Lang. 2017
Empirical software engineering
mining software repositories
0.312017
DéjàVu: a map of code duplicates on GitHub · Proc. ACM Program. Lang. 2017
Software maintenance and evolution
software ecosystems
0.312017
DéjàVu: a map of code duplicates on GitHub · Proc. ACM Program. Lang. 2017

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

corpus analysis · 0.3
YearPublicationVenuePosition
2017 DéjàVu: a map of code duplicates on GitHub
abstract
Previous studies have shown that there is a non-trivial amount of duplication in source code. This paper analyzes a corpus of 4.5 million non-fork projects hosted on GitHub representing over 428 million files written in Java, C++, Python, and JavaScript. We found that this corpus has a mere 85 million unique files. In other words, 70% of the code on GitHub consists of clones of previously created files. There is considerable variation between language ecosystems. JavaScript has the highest rate of file duplication, only 6% of the files are distinct. Java, on the other hand, has the least duplication, 60% of files are distinct. Lastly, a project-level analysis shows that between 9% and 31% of the projects contain at least 80% of files that can be found elsewhere. These rates of duplication have implications for systems built on open source software as well as for researchers interested in analyzing large code bases. As a concrete artifact of this study, we have created DéjàVu, a publicly available map of code duplicates in GitHub repositories.
Cristina V. Lopes, Petr Maj, Pedro Martins 0001, Vaibhav Saini, Di Yang 0001, Jakub Zitny, Hitesh Sajnani, Jan Vitek
Proc. ACM Program. Lang.6