About this document
Machine Learning for Software Fault Prediction by Prabhpahul Singh is a document available to read on EtoBox.
This document compares the predictive capabilities of six machine learning techniques for software fault prediction: Adaboost, Bagging, Decision Tree, LogitBoost, Naive Bayes, and Random Forest. The results are validated on seven open source software datasets. Random Forest is found to be the best technique for developing fault prediction models, outperforming traditional logistic regression. Statistical tests are used to rigorously assess the effectiveness of the machine learning models.
- Author
- Prabhpahul Singh
- Language
- EN