Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance

Software defect prediction (SDP) is the technique used to predict the occurrences of defects in the early stages of software development process. Early prediction of defects will reduce the overall cost of software and also increase its reliability. Most of the defect prediction methods proposed in...

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Bibliographic Details
Published in:Symmetry (Basel) 2020-03, Vol.12 (3), p.407
Main Authors: Bejjanki, Kiran Kumar, Gyani, Jayadev, Gugulothu, Narsimha
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
class imbalance
Classification
Datasets
Decision trees
Defects
Machine learning
Oversampling
Performance evaluation
software defect prediction
Software development
Software engineering
software metrics
Software quality
Software reliability
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Summary:Software defect prediction (SDP) is the technique used to predict the occurrences of defects in the early stages of software development process. Early prediction of defects will reduce the overall cost of software and also increase its reliability. Most of the defect prediction methods proposed in the literature suffer from the class imbalance problem. In this paper, a novel class imbalance reduction (CIR) algorithm is proposed to create a symmetry between the defect and non-defect records in the imbalance datasets by considering distribution properties of the datasets and is compared with SMOTE (synthetic minority oversampling technique), a built-in package of many machine learning tools that is considered a benchmark in handling class imbalance problems, and with K-Means SMOTE. We conducted the experiment on forty open source software defect datasets from PRedict or Models in Software Engineering (PROMISE) repository using eight different classifiers and evaluated with six performance measures. The results show that the proposed CIR method shows improved performance over SMOTE and K-Means SMOTE.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym12030407