Academic Journal
Optimized Software Defect Prediction using SMOTE-Augmented Random Forest on High-Dimensional Code Metrics.
| Τίτλος: | Optimized Software Defect Prediction using SMOTE-Augmented Random Forest on High-Dimensional Code Metrics. |
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| Συγγραφείς: | Yudhistiro, Kukuh |
| Πηγή: | Internet of Things & Artificial Intelligence Journal (IOTA); May2026, Vol. 6 Issue 2, p172-184, 13p |
| Θεματικοί όροι: | Random forest algorithms, Resampling (Statistics), Defect tracking (Computer software development), Machine learning, Software measurement |
| Περίληψη: | Software Defect Prediction (SDP) is crucial for improving software quality and reducing maintenance costs. Class imbalance in SDP datasets often leads to biased models favoring the majority (non-defective) class. This paper proposes a hybrid approach combining the Synthetic Minority Over-sampling Technique (SMOTE) with Random Forest (RF) to address imbalance and enhance prediction accuracy. Using a custom dataset of 500 software modules characterized by 35 source code metrics (34 features + defect label, e.g., Average Blank Lines - abl, McCabe's Cyclomatic Complexity - mcc), we compare our method against baselines: Naive Bayes (NB) and Logistic Regression (LR). Results show our SMOTE+RF achieves perfect performance (Accuracy=0.93, Recall=0.91, F1-Score=0.92, AUC=0.97), outperforming NB (Accuracy=0.95, Recall=0.92) and LR (Accuracy=0.95, Recall=0.95). Compared to baseline studies using vanilla ML algorithms on NASA datasets, where RF accuracies range from 80-90% without imbalance handling, our approach demonstrates superior handling of imbalance and higher defect detection rates. Feature importance analysis highlights metrics like Halstead Total Operands (n) and McCabe's Cyclomatic Complexity (mcc) as key predictors. [ABSTRACT FROM AUTHOR] |
| Copyright of Internet of Things & Artificial Intelligence Journal (IOTA) is the property of Internet of Things & Artificial Intelligence Journal (IOTA) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 194421924 RelevancyScore: 1061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1060.76245117188 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimized Software Defect Prediction using SMOTE-Augmented Random Forest on High-Dimensional Code Metrics. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yudhistiro%2C+Kukuh%22">Yudhistiro, Kukuh</searchLink> – Name: TitleSource Label: Source Group: Src Data: Internet of Things & Artificial Intelligence Journal (IOTA); May2026, Vol. 6 Issue 2, p172-184, 13p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Resampling+%28Statistics%29%22">Resampling (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Software+measurement%22">Software measurement</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Software Defect Prediction (SDP) is crucial for improving software quality and reducing maintenance costs. Class imbalance in SDP datasets often leads to biased models favoring the majority (non-defective) class. This paper proposes a hybrid approach combining the Synthetic Minority Over-sampling Technique (SMOTE) with Random Forest (RF) to address imbalance and enhance prediction accuracy. Using a custom dataset of 500 software modules characterized by 35 source code metrics (34 features + defect label, e.g., Average Blank Lines - abl, McCabe's Cyclomatic Complexity - mcc), we compare our method against baselines: Naive Bayes (NB) and Logistic Regression (LR). Results show our SMOTE+RF achieves perfect performance (Accuracy=0.93, Recall=0.91, F1-Score=0.92, AUC=0.97), outperforming NB (Accuracy=0.95, Recall=0.92) and LR (Accuracy=0.95, Recall=0.95). Compared to baseline studies using vanilla ML algorithms on NASA datasets, where RF accuracies range from 80-90% without imbalance handling, our approach demonstrates superior handling of imbalance and higher defect detection rates. Feature importance analysis highlights metrics like Halstead Total Operands (n) and McCabe's Cyclomatic Complexity (mcc) as key predictors. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Internet of Things & Artificial Intelligence Journal (IOTA) is the property of Internet of Things & Artificial Intelligence Journal (IOTA) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.31763/iota.v6i2.1100 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 172 Subjects: – SubjectFull: Random forest algorithms Type: general – SubjectFull: Resampling (Statistics) Type: general – SubjectFull: Defect tracking (Computer software development) Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Software measurement Type: general Titles: – TitleFull: Optimized Software Defect Prediction using SMOTE-Augmented Random Forest on High-Dimensional Code Metrics. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yudhistiro, Kukuh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 27744353 Numbering: – Type: volume Value: 6 – Type: issue Value: 2 Titles: – TitleFull: Internet of Things & Artificial Intelligence Journal (IOTA) Type: main |
| ResultId | 1 |