Demo Data from VLDB 2026 / reviewers in the wild / expert
Ping-Jung Wu
dblp:37/1104
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2008
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 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.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › document retrieval
cluster-based retrieval |
0.1 | 1 | 2008 | Information retrieval on bug locations by learning co-located bug report clusters · SIGIR 2008 |
Methods — techniques the papers use, named apart from their topics
support vector machine · 0.1co-location shrinkage · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Information retrieval on bug locations by learning co-located bug report clustersabstractBug locating usually involves intensive search activities and incurs unpredictable cost of labor and time. An issue of information retrieval on bug locations is particularly addressed to facilitate identifying bugs from software code. In this paper, a novel bug retrieval approach with co-location shrinkage (CS) is proposed. The proposed approach has been implemented in open-source software projects collected from real-world repositories, and consistently improves the retrieval accuracy of a state-of-the-art Support Vector Machine (SVM) model. Ing-Xiang Chen, Hojun Jaygarl, Cheng-Zen Yang, Ping-Jung Wu |
SIGIR | 4 |