Loading...
Please wait, while we are loading the content...
Similar Documents
A Software Reliability Prediction Model: Using Improved Long Short Term Memory Network
| Content Provider | Semantic Scholar |
|---|---|
| Author | Yangzhen, Fu Lian, Zhang Chenchen, Zeng Chao, Feng |
| Copyright Year | 2017 |
| Abstract | With the development of software reliability research and machine learning, many machine learning models have been used in software reliability prediction. A long short term memory network (LSTM) modeling approach for software reliability prediction is proposed. Profit from its particular data flow control structure, the model overcomes the vanishing and exploding sensitivity of simple recursive neural network for software reliability prediction. Proposed approach also combines with layer normalization and truncate back propagation. To some extent, these two methods promote the effect of the proposed model. Compared with the simple recursive neural network, numerical results show that our proposed approach has a better performance and robustness with respect to software reliability prediction. |
| Starting Page | 614 |
| Ending Page | 615 |
| Page Count | 2 |
| File Format | PDF HTM / HTML |
| DOI | 10.1109/QRS-C.2017.115 |
| Alternate Webpage(s) | http://iranarze.ir/wp-content/uploads/2018/06/E8058-IranArze.pdf |
| Alternate Webpage(s) | https://doi.org/10.1109/QRS-C.2017.115 |
| Journal | 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
