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EDBT 2026 Demo / reviewers in the wild / expert

Michael Patalas

dblp:56/10923 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—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
Software maintenance and evolution · 67% Empirical software engineering · 33%

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

TopicWeightPapersLastEvidence papers
Software maintenance and evolution
code review		 0.212016
Automatically recommending code reviewers based on their expertise: an empirical comparison · ASE 2016
Empirical software engineering
mining software repositories		 0.212016
Automatically recommending code reviewers based on their expertise: an empirical comparison · ASE 2016
Software maintenance and evolution › code review
reviewer recommendation		 0.212016
Automatically recommending code reviewers based on their expertise: an empirical comparison · ASE 2016

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

expertise modeling · 0.2
YearPublicationVenuePosition
2016 Automatically recommending code reviewers based on their expertise: an empirical comparison
abstract
Code reviews are an essential part of quality assurance in Free, Libre, and Open Source (FLOSS) projects. However, finding a suitable reviewer can be difficult, and delayed or forgotten reviews are the consequence. Automating reviewer selection with suitable algorithms can mitigate this problem. We compare empirically six algorithms based on modification expertise and two algorithms based on review expertise on four major FLOSS projects. Our results indicate that the algorithms based on review expertise yield better recommendations than those based on modification expertise. The algorithm Weighted Review Count (WRC) recommends at least one out of five reviewers correctly in 69 % to 75 % of all cases, which is one of the best results achieved in the comparison.
Christoph Hannebauer, Michael Patalas, Sebastian Stünkel, Volker Gruhn
 ASE2