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.