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Ping-Jung Wu

dblp:37/1104 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Information retrieval › document retrieval
cluster-based retrieval
0.112008
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
YearPublicationVenuePosition
2008 Information retrieval on bug locations by learning co-located bug report clusters
abstract
Bug 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
SIGIR4