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

Nikhith Sannidhi

dblp:282/3691 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved

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

Software engineering, systems software and programming languages · 1 · 1 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
Empirical software engineering · 87% Software maintenance and evolution · 13%

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

TopicWeightPapersLastEvidence papers
Empirical software engineering
mining software repositories		 0.612022
Discovering Repetitive Code Changes in Python ML Systems · ICSE 2022
Empirical software engineering › mining software repositories › commit analysis
repetitive code changes		 0.612022
Discovering Repetitive Code Changes in Python ML Systems · ICSE 2022
Software maintenance and evolution
software evolution		 0.212022
Discovering Repetitive Code Changes in Python ML Systems · ICSE 2022

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

mining software repositories · 0.6
YearPublicationVenuePosition
2022 Discovering Repetitive Code Changes in Python ML Systems
abstract
Over the years, researchers capitalized on the repetitiveness of software changes to automate many software evolution tasks. Despite the extraordinary rise in popularity of Python-based ML systems, they do not benefit from these advances. Without knowing what are the repetitive changes that ML developers make, researchers, tool, and library designers miss opportunities for automation, and ML developers fail to learn and use best coding practices.
Malinda Dilhara, Ameya Ketkar, Nikhith Sannidhi, Danny Dig
 ICSE3