Lee, T., Nam, J., Han, D., Kim, S. and In, H.P. (2011) Micro Interaction Metrics for Defect Prediction. Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, September 2011, 311-321.
https://doi.org/10.1145/2025113.2025156

has been cited by the following article:

• TITLE: Software Defect Prediction Using Hybrid Machine Learning Techniques: A Comparative Study

  AUTHORS: Hemant Kumar, Vipin Saxena

  KEYWORDS: Defect Prediction, Hybrid Techniques, Ensemble Models, Machine Learning, Neural Network

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.17 No.4, April 12, 2024

  ABSTRACT: When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies.