SOFTWARE DEFECT PREDICTION USING PCA BASED RECURRENT NEURAL NETWORK

Eka Alifia Kusnanti, Lauretha Devi Fajar Vantie, Umi Laili Yuhana

Abstract


Software quality is one of the important phases in software development. Software quality assesses the usability and quality of the software developed. Defect prediction early in software development helps in software quality assurance by reducing software defects that may occur. With good predictions, it will provide additional benefits in terms of resource and cost efficiency. The researchers in this study have proposed a software defect prediction method that utilizes a Recurrent Neural Network (RNN) based on Principal Component Analysis (PCA). The dataset used is the PROMISE dataset, namely JM1, CM1, PC1, KC1, and KC2. The test results showed that the PCA-RNN method was successfully applied. For the highest accuracy on the PC1 dataset, with an accuracy of 93.99% with the division of training data by testing data (70:30).


References


A. Bahtiar, Mulyawan, Suryani, and D. Firmansyah, “Prediksi Cacat Sofware Menggunakan Algoritma C4.5 Berbasis Particle Swarm Opti-mization,” KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer , vol. 3, 2019.

T. Hidayat, A. F. Habibi, and U. L. Yuhana, “Software Defect Prediction Menggunakan Algoritma K-NN Yang Dioptimasi Dengan PSO,” SCAN - Jurnal Teknologi Informasi dan Komunikasi, vol. 15, no. 1, Feb. 2020, doi: 10.33005/scan.v15i1.1848.

M. Sonhaji Akbar and S. Rochima, “Prediksi Cacat Perangkat Lunak Dengan Optimasi Naive Bayes,” Jurnal Sistem dan Informatika (JSI), vol. 11, pp. 147–155, May 2017.

L. Qiao, G. Li, D. Yu, and H. Liu, “Deep Feature Learning to Quantitative Prediction of Software Defects,” in 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), IEEE, Jul. 2021, pp. 1401–1402. doi: 10.1109/COMPSAC51774.2021.00204.

S. A. Putri and R. S. Wahono, “Integrasi SMOTE dan Information Gain pada Naive Bayes untuk Prediksi Cacat Software,” Journal of Software Engineering, vol. 1, no. 2, 2015, [Online]. Available: http://journal.ilmukomputer.org

E. Borandag, “Software Fault Prediction Using an RNN-Based Deep Learning Approach and Ensemble Machine Learning Techniques,” Applied Sciences, vol. 13, no. 3, p. 1639, Jan. 2023, doi: 10.3390/app13031639.

S. Sinsomboonthong, “Performance comparison of new adjusted min-max with decimal scaling and statistical column normalization meth-ods for artificial neural network classification,” Int J Math Math Sci, vol. 2022, 2022.

P. S. S. Gopi and M. Karthikeyan, “Red fox optimization with ensemble recurrent neural network for crop recommendation and yield prediction model,” Multimed Tools Appl, pp. 1–21, 2023.

D. Sartika and I. Saluza, “Penerapan Metode Principal Component Analysis (PCA) Pada Klasifikasi Status Kredit Nasabah Bank Sumsel Babel Cabang KM 12 Palembang Menggunakan Metode Decision Tree,” Generic, vol. 14, no. 2, pp. 45–49, 2022.

N. Sunarmi, R. Hasanah, R. Fitriana, and I. N. Hamidah, “Analisis Unsur Cuaca pada Pertanian Bawang Merah Kabupaten Nganjuk Tahun 2019 dengan Principal Component Analysis,” in SENKIM: Seminar Nasional Karya Ilmiah Multidisiplin, 2022, pp. 40–50.

H. H. Q. Hayqal, O. Soesanto, and Y. Sukmawaty, “K-Means Clustering dan Principal Component Analysis (PCA) Dalam Radial Basis Function Neural Network (RBFNN) Untuk Klasifikasi Data Multivariat,” Journal of Mathematics: Theory and Applications, pp. 1–7, 2022.

J. Wang, X. Li, J. Li, Q. Sun, and H. Wang, “NGCU: A new RNN model for time-series data prediction,” Big Data Research, vol. 27, p. 100296, 2022.

A. Ajitha, M. Goel, M. Assudani, S. Radhika, and S. Goel, “Design and development of Residential Sector Load Prediction model during COVID-19 Pandemic using LSTM based RNN,” Electric Power Systems Research, vol. 212, p. 108635, 2022.

N. M. Shahani, M. Kamran, X. Zheng, and C. Liu, “Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA,” Pet Sci Technol, vol. 40, no. 5, pp. 534–555, 2022.

I. Amalou, N. Mouhni, and A. Abdali, “Multivariate time series prediction by RNN architectures for energy consumption forecasting,” Energy Reports, vol. 8, pp. 1084–1091, 2022.

A. Wicaksono, “Prediksi dan Deteksi Bug Pada Software Menggunakan Pendekatan Machine Learning: Machine Learning,” JURNAL SIGN IN: Jurnal Ilmiah Sistem Informasi dan Informatika, vol. 2, no. 2, pp. 14–17, 2023.

S. Goyal, “Effective software defect prediction using support vector machines (SVMs),” International Journal of System Assurance Engi-neering and Management, vol. 13, no. 2, pp. 681–696, 2022.

N. Hidayati, J. Suntoro, and G. G. Setiaji, “Perbandingan Algoritma Klasifikasi untuk Prediksi Cacat Software dengan Pendekatan CRISP-DM,” Jurnal Sains dan Informatika, vol. 7, no. 2, pp. 117–126, 2021.

S. K. Pemmada, H. S. Behera, J. Nayak, and B. Naik, “Correlation-based modified long short-term memory network approach for software defect prediction,” Evolving Systems, vol. 13, no. 6, pp. 869–887, 2022.

M. Shafiq, F. H. Alghamedy, N. Jamal, T. Kamal, Y. I. Daradkeh, and M. Shabaz, “Scientific programming using optimized machine learn-ing techniques for software fault prediction to improve software quality,” IET Software, pp. 1–11, 2023.

M. Mustaqeem and M. Saqib, “Principal component based support vector machine (PC-SVM): a hybrid technique for software defect detection,” Cluster Comput, vol. 24, no. 3, pp. 2581–2595, 2021.

M. Shah and N. Pujara, “A review on software defects prediction methods,” arXiv preprint arXiv:2011.00998, 2020.

A. Nawaz, A. U. Rehman, and M. Abbas, “A novel multiple ensemble learning models based on different datasets for software defect prediction,” arXiv preprint arXiv:2008.13114, 2020.




DOI: http://dx.doi.org/10.12962/j24068535.v22i1.a1199

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