Diterbitkan Oleh:
Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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PENERAPAN FEATURE SELECTION PADA BAYESIAN NETWORK UNTUK PREDIKSI CACAT PERANGKAT LUNAK
Keywords:
Bayesian Network, Feature Selection, Software Defect Prediction, Naive Bayesian
Abstract
Pada perkembangannya penelitian pada bidang prediksi cacat perangkat lunak, semakin banyak diminati oleh para peneliti. Untuk mengurangi biaya perawatan dan menjaga kualitas perangkat lunak. Salah satunya dengan, pemilihan modul cacat dan tidak cacat pada perangkat lunak menggunakan machine learning. Salah satunya adalah machine learning Bayesian Network, yang memiliki kinerja lebih baik dari Naive Bayesian. Seperti yang telah dilakukan pada penelitian ini, bahwa Bayesian Network dengan mengintegrasikan algoritma pemilihan atribute seperti Chi Square, Information Gain dan Relief. Model tersebut dapat menghasilkan tingkat akurasi hingga 0,9 % pada salah satu dataset Nasa yang digunakan pada penelitian ini. Oleh karenanya kinerja dan tingkat akurasi Bayesian Network pada prediksi cacat perangkat lunak sangat baik.
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References
Bouguila, N., Wang, J. H., & Hamza, A. Ben. (2008). A Bayesian Approach for Software Quality Prediction. In Intelligent Systems, 2008. IS ’08. 4th International IEEE Conference (pp. 49–54). http://doi.org/10.1109/IS.2008.4670508
Catal, C. (2011). Software fault prediction: A literature review and current trends. Expert Systems with Applications, 38(4), 4626–4636. http://doi.org/10.1016/j.eswa.2010.10.024
de Carvalho, A. B., Pozo, A., & Vergilio, S. R. (2010). A symbolic fault-prediction model based on multiobjective particle swarm optimization. Journal of Systems and Software, 83(5), 868–882. http://doi.org/10.1016/j.jss.2009.12.023
Demsar, J. (2006). Statistical Comparisons of Classifiers over Multiple Data Sets. The Journal of Machine Learning Research, 7, 1–30.
Fenton, N., Krause, P., & Mishra, R. (2007). Predicting software defects in varying development lifecycles using Bayesian nets. Information and Software Technology, 49, 32–43. http://doi.org/10.1016/j.infsof.2006.09.001
Gao, K., & Khoshgoftaar, T. M. (2011). Software Defect Prediction for High-Dimensional and Class-Imbalanced Data. Conference: Proceedings of the 23rd International Conference on Software Engineering & Knowledge Engineering, (2).
Gao, K., Khoshgoftaar, T. M., Wang, H., & Seliya, N. (2011). Choosing software metrics for defect prediction : an investigation on feature selection techniques. Software: Practice and Experience, 41(5), 579–606. http://doi.org/10.1002/spe
Kabir, M., & Murase, K. (2012). Expert Systems with Applications A new hybrid ant colony optimization algorithm for feature selection. Expert Systems With Applications, 39(3), 3747–3763. http://doi.org/10.1016/j.eswa.2011.09.073
Lessmann, S., Member, S., Baesens, B., Mues, C., & Pietsch, S. (2008). Benchmarking Classification Models for Software Defect Prediction : A Proposed Framework and Novel Findings. IEEE Transactions on Software Engineering, 34(4), 485–496.
Ling, C. X. (2003). Using AUC and Accuracy in Evaluating Learning Algorithms, 1–31.
Ling, C. X., & Zhang, H. (2003). AUC: a statistically consistent and more discriminating measure than accuracy. Proceedings of the 18th International Joint Conference on Artificial Intelligence.
Menzies, T., Greenwald, J., & Frank, A. (2007). Data Mining Static Code Attributes to Learn Defect Predictors. IEEE Transactions on Software Engineering, 33(1), 2–13. http://doi.org/10.1109/TSE.2007.256941
Putri, S. A. (2017). Combining Integreted Sampling Technique with Feature Selection for Software Defect Prediction. In 2017 5th International Conference on Cyber and IT Service Management (CITSM) (pp. 1–6). Denpasar, Indonesia. http://doi.org/10.1109/CITSM.2017.8089264
Putri, S., A. (2017). Laporan Akhir Penelitian Mandiri. Jakarta: STMIK Nusa Mandiri Jakarta
Putri, S. A., & Wahono, R. S. (2015). Integrasi SMOTE dan Information Gain pada Naive Bayes untuk Prediksi Cacat Software. Journal of Software Engineering, 1(2), 86–91.
Shepperd, M., Song, Q., Sun, Z., & Mair, C. (2013). Data Quality : Some Comments on the NASA Software Defect Data Sets. Software Engineering, IEEE Transactions, 39(9), 1–13.
Song, Q., Jia, Z., Shepperd, M., Ying, S., & Liu, J. (2011). A General Software Defect-Proneness Prediction Framework. IEEE Transactions on Software Engineering, 37(3), 356–370. http://doi.org/10.1109/TSE.2010.90
Sun, L., & Erath, A. (2015). A Bayesian network approach for population synthesis. TRANSPORTATION RESEARCH, 61, 49–62. http://doi.org/10.1016/j.trc.2015.10.010
Wang, S., Gao, R., & Wang, L. (2016). Bayesian network classifiers based on Gaussian kernel density. Expert Systems with Applications: An International Journal, 51(C), 207–217. http://doi.org/10.1016/j.eswa.2015.12.031
Catal, C. (2011). Software fault prediction: A literature review and current trends. Expert Systems with Applications, 38(4), 4626–4636. http://doi.org/10.1016/j.eswa.2010.10.024
de Carvalho, A. B., Pozo, A., & Vergilio, S. R. (2010). A symbolic fault-prediction model based on multiobjective particle swarm optimization. Journal of Systems and Software, 83(5), 868–882. http://doi.org/10.1016/j.jss.2009.12.023
Demsar, J. (2006). Statistical Comparisons of Classifiers over Multiple Data Sets. The Journal of Machine Learning Research, 7, 1–30.
Fenton, N., Krause, P., & Mishra, R. (2007). Predicting software defects in varying development lifecycles using Bayesian nets. Information and Software Technology, 49, 32–43. http://doi.org/10.1016/j.infsof.2006.09.001
Gao, K., & Khoshgoftaar, T. M. (2011). Software Defect Prediction for High-Dimensional and Class-Imbalanced Data. Conference: Proceedings of the 23rd International Conference on Software Engineering & Knowledge Engineering, (2).
Gao, K., Khoshgoftaar, T. M., Wang, H., & Seliya, N. (2011). Choosing software metrics for defect prediction : an investigation on feature selection techniques. Software: Practice and Experience, 41(5), 579–606. http://doi.org/10.1002/spe
Kabir, M., & Murase, K. (2012). Expert Systems with Applications A new hybrid ant colony optimization algorithm for feature selection. Expert Systems With Applications, 39(3), 3747–3763. http://doi.org/10.1016/j.eswa.2011.09.073
Lessmann, S., Member, S., Baesens, B., Mues, C., & Pietsch, S. (2008). Benchmarking Classification Models for Software Defect Prediction : A Proposed Framework and Novel Findings. IEEE Transactions on Software Engineering, 34(4), 485–496.
Ling, C. X. (2003). Using AUC and Accuracy in Evaluating Learning Algorithms, 1–31.
Ling, C. X., & Zhang, H. (2003). AUC: a statistically consistent and more discriminating measure than accuracy. Proceedings of the 18th International Joint Conference on Artificial Intelligence.
Menzies, T., Greenwald, J., & Frank, A. (2007). Data Mining Static Code Attributes to Learn Defect Predictors. IEEE Transactions on Software Engineering, 33(1), 2–13. http://doi.org/10.1109/TSE.2007.256941
Putri, S. A. (2017). Combining Integreted Sampling Technique with Feature Selection for Software Defect Prediction. In 2017 5th International Conference on Cyber and IT Service Management (CITSM) (pp. 1–6). Denpasar, Indonesia. http://doi.org/10.1109/CITSM.2017.8089264
Putri, S., A. (2017). Laporan Akhir Penelitian Mandiri. Jakarta: STMIK Nusa Mandiri Jakarta
Putri, S. A., & Wahono, R. S. (2015). Integrasi SMOTE dan Information Gain pada Naive Bayes untuk Prediksi Cacat Software. Journal of Software Engineering, 1(2), 86–91.
Shepperd, M., Song, Q., Sun, Z., & Mair, C. (2013). Data Quality : Some Comments on the NASA Software Defect Data Sets. Software Engineering, IEEE Transactions, 39(9), 1–13.
Song, Q., Jia, Z., Shepperd, M., Ying, S., & Liu, J. (2011). A General Software Defect-Proneness Prediction Framework. IEEE Transactions on Software Engineering, 37(3), 356–370. http://doi.org/10.1109/TSE.2010.90
Sun, L., & Erath, A. (2015). A Bayesian network approach for population synthesis. TRANSPORTATION RESEARCH, 61, 49–62. http://doi.org/10.1016/j.trc.2015.10.010
Wang, S., Gao, R., & Wang, L. (2016). Bayesian network classifiers based on Gaussian kernel density. Expert Systems with Applications: An International Journal, 51(C), 207–217. http://doi.org/10.1016/j.eswa.2015.12.031
Published
2017-09-15
How to Cite
Putri, S., & Larasati, D. (2017). PENERAPAN FEATURE SELECTION PADA BAYESIAN NETWORK UNTUK PREDIKSI CACAT PERANGKAT LUNAK. Jurnal Pilar Nusa Mandiri, 13(2), 275-280. Retrieved from https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/250
Section
Articles
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