Improving Failure Prediction by Ensembling the Decisions of Machine Learning Models: A Case Study
The complexity of software has grown considerably in recent years, making it nearly impossible to detect all faults before pushing to production. Such faults can ultimately lead to failures at runtime. Recent works have shown that using Machine Learning (ML) algorithms it is possible to create model...
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| Outros Autores: | , |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2019
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| Texto completo: | http://hdl.handle.net/10316/101614 |
| País: | Portugal |
| Oai: | oai:estudogeral.sib.uc.pt:10316/101614 |