Feature Transformation for Improved Software Bug Detection Models

Author:

Cynthia Shamse Tasnim1,Roy Banani1,Mondal Debajyoti1

Affiliation:

1. University of Saskatchewan, Canada

Publisher

ACM

Reference47 articles.

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2. 2020. Novel XGBoost Tuned Machine Learning Model for Software Bug Prediction . In Proceedings of International Conference on Intelligent Engineering and Management, ICIEM 2020 . Institute of Electrical and Electronics Engineers Inc., 376–380. https://doi.org/10.1109/ICIEM48762. 2020 .9160152 2020. Novel XGBoost Tuned Machine Learning Model for Software Bug Prediction. In Proceedings of International Conference on Intelligent Engineering and Management, ICIEM 2020. Institute of Electrical and Electronics Engineers Inc., 376–380. https://doi.org/10.1109/ICIEM48762.2020.9160152

3. Feature selection using principal component analysis and genetic algorithm

4. Miltiadis Allamanis , Henry Jackson-Flux , and Marc Brockschmidt . 2021. Self-Supervised Bug Detection and Repair. (may 2021 ). arxiv:2105.12787http://arxiv.org/abs/2105.12787 Miltiadis Allamanis, Henry Jackson-Flux, and Marc Brockschmidt. 2021. Self-Supervised Bug Detection and Repair. (may 2021). arxiv:2105.12787http://arxiv.org/abs/2105.12787

5. Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

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