A Complete Study of Different Models for Software Fault Prediction

Authors

  • Meetesh Nevendra

DOI:

https://doi.org/10.37591/josettt.v5i2.1747

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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Published

2018-09-26

Issue

Vol. 5 No. 2 (2018)

Section

Research Articles

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