Name
Abstract | ||
|---|---|---|
Deep neural networks (DNNs) have been increasingly adopted in various applications. Systematic verification and validation is essential to guarantee the quality of such systems. Due to the scalability problem, formal methods can hardly be widely applied in practice. Testing is one of feasible solutions. However, lacking of input space specification for DNNs requires a large set of test cases to be... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TASE49443.2020.00020 | 2020 International Symposium on Theoretical Aspects of Software Engineering (TASE) |
Keywords | DocType | ISBN |
Deep Neural Network,Test Case Prioritization,Classifier,Feature Extraction | Conference | 978-1-7281-4086-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Zhang | 1 | 0 | 0.34 |
| Yongtai Zhang | 2 | 0 | 0.34 |
| Liwei Zhang | 3 | 0 | 0.68 |
| Hongyu Gao | 4 | 0 | 1.01 |
| Rongjie Yan | 5 | 0 | 0.34 |
| Jun Yan | 6 | 14 | 6.32 |