SMOTE-Based Homogeneous Prediction for Aging-Related Bugs in Cloud-Oriented Software

Harguneet Kaur and Arvinder Kaur
Additional contact information
Harguneet Kaur: University School of Information, Communication and Technology, GGSIPU, Dwarka, New Delhi, India
Arvinder Kaur: University School of Information, Communication and Technology, GGSIPU, Dwarka, New Delhi, India

Journal of Information & Knowledge Management (JIKM), 2023, vol. 22, issue 05, 1-18

Abstract: Software aging is the process caused by Aging-Related Bugs (ARBs) which leads to the depletion of resources and degradation of performance in the long run. ARBs are difficult to find and replicate in future studies as they are less in number, thus prediction of ARB is necessary to save cost and time in the testing phase. ARBs are present in low proportion as compared to non-ARBs known as the class Imbalance problem resulting in insufficient training dataset for prediction models. In this study, Synthetic Minority Oversampling Technique (SMOTE) is applied along with homogeneous cross-project ARB prediction to reduce the effect of imbalance problem in software. SMOTE is oversampling of the minority instances synthetically to balance the dataset and improve the capability of defect prediction models. Homogeneous cross-project prediction is implemented where the datasets are different but the distribution of metric sets of both training and testing datasets is similar. The experiment is conducted on five cloud-oriented software such as Cassandra, Hive, Storm, Hadoop HDFS and Hadoop Mapreduce. The novelty of this study is the combination of SMOTE and homogeneous cross-project defect prediction for ARBs in cloud-oriented software. The comparative analysis is also conducted to understand the difference between SMOTE and non-SMOTE results with the help of machine learning classifiers. The result conveys that SMOTE is an efficient method to address class imbalance problem in ARB prediction.

Keywords: SMOTE; imbalance; cross-project; aging related bug; prediction; software aging (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219649223500478
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:jikmxx:v:22:y:2023:i:05:n:s0219649223500478

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219649223500478

Access Statistics for this article

Journal of Information & Knowledge Management (JIKM) is currently edited by Professor Suliman Hawamdeh

More articles in Journal of Information & Knowledge Management (JIKM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().