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

Rafat Bin Islam

dblp:311/8866 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

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
Software maintenance and evolution · 50% Empirical software engineering · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software maintenance and evolution › issue tracking
bug report summarization		 0.512021
Automatically Annotating Sentences for Task-specific Bug Report Summarization · ASE 2021
Empirical software engineering
mining software repositories		 0.512021
Automatically Annotating Sentences for Task-specific Bug Report Summarization · ASE 2021

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

automated annotation · 0.5
YearPublicationVenuePosition
2021 Automatically Annotating Sentences for Task-specific Bug Report Summarization
abstract
There is a need to summarize bug reports as they can become long due to many comments from conversations between developers and various DevOps tools. Although automated approaches to bug report summarization have been developed, we believe they aim at the wrong target - getting as close as possible to a gold-standard summary. Instead, researchers should create automated bug report annotation approaches that allow project members to create summaries based on their task-specific information needs. We present such an approach.
Akalanka Galappaththi, John Anvik, Rafat Bin Islam
 ASE3