On the Value of Oversampling for Deep Learning in Software Defect Prediction
| Autor: | Rahul Yedida, Tim Menzies |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Feature engineering Computer science business.industry Pipeline (computing) Deep learning 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Fuzzy logic Software Engineering (cs.SE) Computer Science - Software Engineering Software bug Sampling (signal processing) 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering Oversampling Artificial intelligence business computer Software |
| Zdroj: | IEEE Transactions on Software Engineering. 48:3103-3116 |
| ISSN: | 2326-3881 0098-5589 |
| DOI: | 10.1109/tse.2021.3079841 |
| Popis: | One truism of deep learning is that the automatic feature engineering (seen in the first layers of those networks) excuses data scientists from performing tedious manual feature engineering prior to running DL. For the specific case of deep learning for defect prediction, we show that that truism is false. Specifically, when we preprocess data with a novel oversampling technique called fuzzy sampling, as part of a larger pipeline called GHOST (Goal-oriented Hyper-parameter Optimization for Scalable Training), then we can do significantly better than the prior DL state of the art in 14/20 defect data sets. Our approach yields state-of-the-art results significantly faster deep learners. These results present a cogent case for the use of oversampling prior to applying deep learning on software defect prediction datasets. Comment: v3, revision 2 (minor revision); submitted to TSE |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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