LINEAR REGRESSION DENGAN PEMBOBOTAN ATRIBUT DENGAN METODE PSO UNTUK SOFTWARE DEFECT PREDICTION
Abstract
Kualitas software sudah menjadi bagian yang penting dalam proses pengembangan. Karena semakin kompleksnya sebuah software dan tingginya ekspektasi dari pelanggan. Maka saat ini biaya pengembangan software juga semakin tinggi. Oleh karena itu dibutuhkan efisiensi untuk menekan biaya pengembangan software. Salah satu cara yang bisa dilakukan yaitu dengan software defect prediction. Dengan sotware defect prediction maka dapat diketahui proyek software mana yang butuh pengecekan lebih intens. Tim test software dapat mengalokasikan waktu dan biaya lebih efektif berdasarkan hasil dari model algoritma. Metode pada riset ini menggunakan prepocessing dengan mengoptimalkan bobot atribut dengan menggunakan metode PSO yang merupakan algoritma pencarian berbasis populasi dan yang diinisialisi dengan populasi solusi acak yang disebut partikel. Berdasarkan hasil pengolaha data dengan metode preprocessing terhadap dataset NASA MDP CM1. Maka didapatkan metode preprocessing dengan pembobotan atribut dengan metode PSO memiliki peningkatan akurasi menjadi 86.37% dari sebelumnya 85.54% dan AUC menjadi 0.827 dari sebelumnya 0.762.
Kata kunci: prediksi cacat software, linier regression, feature selection, optimize weight.
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Q. Song, Z. Jia, M. Shepperd, S. Ying, J. Liu, A general software defect-pronenessprediction framework, IEEE Trans. Softw. Eng. 37 (3) (2011) 356–370.
O.F. Arar, K. Ayan, Software defect prediction using cost-sensitive neuralnetwork, Appl. Soft Comput. 33 (C) (2015) 263–277.
P. Michaels, Faulty Software Can Lead to Astronomic Costs, 2008, http://www.computerweekly.com/opinion/Faulty-software-can-lead-to-astronomic-costs, ComputerWeekly.com (retrieved 23.02.14).
S. Dick, A. Meeks, M. Last, H. Bunke, A. Kandel, Data mining in software metricsdatabases Fuzzy Sets Syst. 145 (1) (2004) 81–110.
L. Pelayo, S. Dick, Applying novel resampling strategies to software defect pre-diction, in: IEEE Fuzzy Information Processing Society, NAFIPS’07, San Diego,USA, June 24–27, 2007, pp. 69–72.
J.D Lovelock, IT Spending Forecast, 2Q16 Update Gartner http://www.gartner.com/technology/research/it-spending-forecast/
G. Denaro, “Estimating software fault-proneness for tuning testing activities,” in Proceedings of the 22nd International Conference on Software engineering - ICSE ’00, 2000, pp. 704–706.
T. M. Khoshgoftaar, N. Seliya, and K. Gao, “Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study,” Empir. Softw. Eng., vol. 10, no. 2, pp. 183–218, Apr. 2005.
Gary D. Boetticher, “Nearest Neighbor Sampling for Better Defect Prediction”, ACM SIGSOFT Software Engineering Notes, vol 30, page 1-6, July 2005
T. Menzies, J. Greenwald, and A. Frank, “Data Mining Static Code Attributes to Learn Defect Predictors,” IEEE Trans. Softw. Eng., vol. 33, no. 1, pp. 2–13, Jan. 2007.
Ishani Aroraa, Vivek Tetarwala,Anju Sahaa, “Open Issues in Software Defect Prediction”, Procedia Computer Science 46, 906 – 912, 2015
C. Catal and B. Diri, “Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem,” Inf. Sci. (Ny)., vol. 179, no. 8, pp. 1040–1058, Mar. 2009.
D. Gray, D. Bowes, N. Davey, Y. Sun, and B. Christianson, “Reflections on the NASA MDP data sets,” IET Softw., vol. 6, no. 6, p. 549, 2012.
M. H. Halstead, Elements of Software Science, vol. 7. Elsevier, 1977, p. 127.
T. J. McCabe, “A Complexity Measure,” IEEE Trans. Softw. Eng., vol. SE-2, no. 4, pp. 308–320, 1976.
I. H. Witten, E. Frank, and M. A. Hall, Data Mining Third Edition. Elsevier Inc., 2011.
Lessmann, S., Baesens, B., Mues, C., & Pietsch, S. “Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings.” IEEE Transactions on Software Engineering, 34(4), 485–496, 2008
A. Abraham, C. Grosan and V. Ramos, Swarm Intelligence In Data Mining, Verlag Berlin Heidelberg: Springer, 2006.
S. Bibi and others, ‘Regression via Classification Applied on Software Defect Estimation’, Expert Systems with Applications, 34.3 (2008), 2091–2101.
Stefan Lessmann and others, ‘Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings’, IEEE Transactions on Software Engineering, 34.4 (2008), 485–96.
Jun Zheng, ‘Cost-Sensitive Boosting Neural Networks for Software Defect Prediction’, Expert Systems with Applications, 37.6 (2010), 4537–43.
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