Name
Abstract | ||
|---|---|---|
Accurate software bug number prediction makes software test resource allocation, maintenance, and release time cost efficient. However, it is a challenge to accurately predict the number of software bugs when there fluctuations caused by many uncertain factors faced by the complex software. Considering this, a new method for software bug number prediction based on a panel data model from the persp... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TR.2022.3149658 | IEEE Transactions on Reliability |
Keywords | DocType | Volume |
Codes,Linux,Computer bugs,Complex networks,Predictive models,Maintenance engineering,Software | Journal | 71 |
Issue | ISSN | Citations |
1 | 0018-9529 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shunkun Yang | 1 | 31 | 12.25 |
| Xiaodong Gou | 2 | 3 | 3.78 |
| Minghao Yang | 3 | 1 | 1.37 |
| Qi Shao | 4 | 0 | 2.70 |
| Chong Bian | 5 | 0 | 2.37 |
| Ming Jiang | 6 | 0 | 0.34 |
| Yongjie Qiao | 7 | 0 | 0.34 |