Seml: A Semantic LSTM Model for Software Defect Prediction
Software defect prediction can assist developers in finding potential bugs and reducing maintenance cost. Traditional approaches usually utilize software metrics (Lines of Code, Cyclomatic Complexity, etc.) as features to build classifiers and identify defective software modules. However, software m...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8747001/ |