Improving with Hybrid Feature Selection in Software Defect Prediction
Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature conve...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Department of Informatics, UIN Sunan Gunung Djati Bandung
2024-04-01
|
| Series: | JOIN: Jurnal Online Informatika |
| Subjects: | |
| Online Access: | https://join.if.uinsgd.ac.id/index.php/join/article/view/1307 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849335810833252352 |
|---|---|
| author | Muhammad Yoga Adha Pratama Rudy Herteno Mohammad Reza Faisal Radityo Adi Nugroho Friska Abadi |
| author_facet | Muhammad Yoga Adha Pratama Rudy Herteno Mohammad Reza Faisal Radityo Adi Nugroho Friska Abadi |
| author_sort | Muhammad Yoga Adha Pratama |
| collection | DOAJ |
| description | Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. So this research focuses on improving PSO performance by using feature selection methods with hybrid techniques to overcome these problems. The feature selection techniques used are Filter and Wrapper. The methods used are Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Forward Selection (FS) because feature selection methods have been proven to overcome data dimensionality problems and eliminate noisy attributes. Feature selection is often used by some researchers to overcome these problems, because these methods have an important function in the process of reducing data dimensions and eliminating uncorrelated attributes that can cause noisy. Naive Bayes algorithm is used to support the process of determining the most optimal class. Performance evaluation will use AUC with an alpha value of 0.050. This hybrid feature selection technique brings significant improvement to PSO performance with a much lower AUC value of 0.00342. Comparison of the significance of AUC with other combinations shows the value of FS PSO of 0.02535, CFS FS PSO of 0.00180, and CS FS PSO of 0.01186. The method in this study contributes to improving PSO in the SDP domain by significantly increasing the AUC value. Therefore, this study highlights the potential of feature selection with hybrid techniques to improve PSO performance in SDP. |
| format | Article |
| id | doaj-art-ffe3f3dbe1d147e88f2c9903ba03645c |
| institution | Kabale University |
| issn | 2528-1682 2527-9165 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Department of Informatics, UIN Sunan Gunung Djati Bandung |
| record_format | Article |
| series | JOIN: Jurnal Online Informatika |
| spelling | doaj-art-ffe3f3dbe1d147e88f2c9903ba03645c2025-08-20T03:45:10ZengDepartment of Informatics, UIN Sunan Gunung Djati BandungJOIN: Jurnal Online Informatika2528-16822527-91652024-04-0191526010.15575/join.v9i1.13071308Improving with Hybrid Feature Selection in Software Defect PredictionMuhammad Yoga Adha Pratama0Rudy Herteno1Mohammad Reza Faisal2Radityo Adi Nugroho3Friska Abadi4Department of Computer Science, University of Lambung Mangkurat, Kalimantan SelatanDepartment of Computer Science, University of Lambung Mangkurat, Kalimantan SelatanDepartment of Computer Science, University of Lambung Mangkurat, Kalimantan SelatanDepartment of Computer Science, University of Lambung Mangkurat, Kalimantan SelatanDepartment of Computer Science, University of Lambung Mangkurat, Kalimantan SelatanSoftware defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. So this research focuses on improving PSO performance by using feature selection methods with hybrid techniques to overcome these problems. The feature selection techniques used are Filter and Wrapper. The methods used are Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Forward Selection (FS) because feature selection methods have been proven to overcome data dimensionality problems and eliminate noisy attributes. Feature selection is often used by some researchers to overcome these problems, because these methods have an important function in the process of reducing data dimensions and eliminating uncorrelated attributes that can cause noisy. Naive Bayes algorithm is used to support the process of determining the most optimal class. Performance evaluation will use AUC with an alpha value of 0.050. This hybrid feature selection technique brings significant improvement to PSO performance with a much lower AUC value of 0.00342. Comparison of the significance of AUC with other combinations shows the value of FS PSO of 0.02535, CFS FS PSO of 0.00180, and CS FS PSO of 0.01186. The method in this study contributes to improving PSO in the SDP domain by significantly increasing the AUC value. Therefore, this study highlights the potential of feature selection with hybrid techniques to improve PSO performance in SDP.https://join.if.uinsgd.ac.id/index.php/join/article/view/1307software defect predictionparticle swarm optimizationfeature selectionfilterwrappernaive bayes |
| spellingShingle | Muhammad Yoga Adha Pratama Rudy Herteno Mohammad Reza Faisal Radityo Adi Nugroho Friska Abadi Improving with Hybrid Feature Selection in Software Defect Prediction JOIN: Jurnal Online Informatika software defect prediction particle swarm optimization feature selection filter wrapper naive bayes |
| title | Improving with Hybrid Feature Selection in Software Defect Prediction |
| title_full | Improving with Hybrid Feature Selection in Software Defect Prediction |
| title_fullStr | Improving with Hybrid Feature Selection in Software Defect Prediction |
| title_full_unstemmed | Improving with Hybrid Feature Selection in Software Defect Prediction |
| title_short | Improving with Hybrid Feature Selection in Software Defect Prediction |
| title_sort | improving with hybrid feature selection in software defect prediction |
| topic | software defect prediction particle swarm optimization feature selection filter wrapper naive bayes |
| url | https://join.if.uinsgd.ac.id/index.php/join/article/view/1307 |
| work_keys_str_mv | AT muhammadyogaadhapratama improvingwithhybridfeatureselectioninsoftwaredefectprediction AT rudyherteno improvingwithhybridfeatureselectioninsoftwaredefectprediction AT mohammadrezafaisal improvingwithhybridfeatureselectioninsoftwaredefectprediction AT radityoadinugroho improvingwithhybridfeatureselectioninsoftwaredefectprediction AT friskaabadi improvingwithhybridfeatureselectioninsoftwaredefectprediction |