Demo Data from EDBT 2026 / reviewers in the wild / expert
Yuanlei Han
dblp:282/2381
· DBLP profile ↗
2ranked-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 · 2 · 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 testing · 100% | |
| Artificial intelligence
1 paper |
Transfer learning and domain adaptation · 50% Efficient and distributed learning · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing
deep learning testing |
0.4 | 1 | 2020 | Multiple-Boundary Clustering and Prioritization to Promote Neural Network Retraining · ASE 2020 |
Software testing › test process › test design
test input selection |
0.4 | 1 | 2020 | Multiple-Boundary Clustering and Prioritization to Promote Neural Network Retraining · ASE 2020 |
Machine learning › Efficient and distributed learning
data selection |
0.1 | 1 | 2020 | Multiple-Boundary Clustering and Prioritization to Promote Neural Network Retraining · ASE 2020 |
Machine learning › Transfer learning and domain adaptation › model adaptation
model retraining |
0.1 | 1 | 2020 | Multiple-Boundary Clustering and Prioritization to Promote Neural Network Retraining · ASE 2020 |
Methods — techniques the papers use, named apart from their topics
prioritization · 0.9clustering · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Boundary sampling to boost mutation testing for deep learning models
Weijun Shen, Yanhui Li 0001, Yuanlei Han, Lin Chen 0015, Di Wu 0014, Yuming Zhou, Baowen Xu |
Inf. Softw. Technol. | 3 |
| 2020 | Multiple-Boundary Clustering and Prioritization to Promote Neural Network RetrainingabstractWith the increasing application of deep learning (DL) models in many safety-critical scenarios, effective and efficient DL testing techniques are much in demand to improve the quality of DL models. One of the major challenges is the data gap between the training data to construct the models and the testing data to evaluate them. To bridge the gap, testers aim to collect an effective subset of inputs from the testing contexts, with limited labeling effort, for retraining DL models. Weijun Shen, Yanhui Li 0001, Lin Chen 0015, Yuanlei Han, Yuming Zhou, Baowen Xu |
ASE | 4 |