A Complete Study of Different Models for Software Fault Prediction
Abstract
Context- Software faults prediction improves software quality, software reliability, and software efficiency by early identification of faults. Numerous classification methods have been suggested for this task.
Objective- The main objectives of the studies are (i) find the number of defects in AEEEM defect datasets, (ii) Challenges faced with Imbalanced Datasets.
Method- We use AEEEM defect datasets for the prediction of faulty classes using four deferent machine learning technique. This particular paper present software fault prediction problem using individual and ensemble approach.
Result- The results show that the balancing using SMOTE with random forest and AdaBoost as ensemble classifier has more predictive capability for predicting faults.
Conclusion- The results ensure that the predictive capability of various machine learning techniques with regard to developing fault prediction models
Keywords: SMOTE, AdaBoost, ADASYN, Transfer component analysis (TCA), random forest (RF), Fuzzy C Means (FCM)
Cite this Article
Meetesh Nevendra, Pradeep Singh. A Complete Study of Different Models forSoftware Fault Prediction. Journal of Software Engineering Tools & Technology Trends. 2018; 5(2): 1–10p.
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