@article{2953,
  author = {Anil Kumar Pandey, Manjari Gupta},
  title = {Software Fault Prediction using Ensemble Techniques},
  journal = {Journal of Networking Technology},
  year = {2020},
  volume = {11},
  number = {1},
  doi = {https://doi.org/10.6025/jnt/2020/11/1/25-41},
  url = {http://www.dline.info/jnt/fulltext/v11n1/jntv11n1_3.pdf},
  abstract = {In recent years, software fault classification became an important research area for the development of reliable and high quality software products. The fault classification helps in identification of fault software modules and allows the developers to concentrate on that module. It helps the developer to save the time and control financial losses to industry. Therefore, in this paper, a novel fault classification method based on feature ranking algorithms and ensemble techniques is proposed. The number of features available in the metrics are selected to represent the fault using feature ranking algorithms and operated on ensemble techniques to check the performance. Also, various hyper parameters are tuned for the ensemble techniques to identify the best model. The experimental result demonstrates the good results for bagging with K-nearest neighbor and random forest in comparison with other methods.},
}