博碩士論文 110423049 詳細資訊




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姓名 林庭伊(Ting-Yi Lin)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 應用混合式前處理與 IPF 過濾器之集成式學習 於軟體缺陷預測
(An Application of Hybrid-Sampling and Iterative-Partitioning Filters for Ensemble Learning in Software Defect Predictio)
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摘要(中) 隨著軟體規模的增長,測試成本也會越來越高,為避免測試階段造成軟體缺陷的檢
查遺漏而導致嚴重後果,機器學習開始被使用於軟體缺陷預測(Software Defect
Prediction ,簡稱 SDP) 並嘗試與現今的自動化測試工具結合,利用機器學習協助且及
早定位容易出現錯誤的模組,藉此將測試資源集中於特定的專案模組上,讓企業得以利
用更低成本,產出更高品質的產品。本研究使用 EE-IPF(EasyEnsemble +IterativePartitioning Filter, IPF 迭代分層過濾器)架構與三種不同過採樣方式結合,分別為
Polynom-fit-SMOTE 、ProWsyn 、SMOTEIPF 形 成 Hybrid-EE-IPF 架構應用於 SDP 領
域。希望藉由此方式改善 EasyEnsemble 模型中單一隨機欠採樣上可能造成資訊缺失與
少類學習特徵不足的問題,且不同於過往 SDP 研 究使用單一 IPF 過濾器過濾雜訊資料
點,而是將多個過濾器與集成模型結合,以提升各 基底分類的多樣性,進而改善軟體
缺陷上的預測表現。
摘要(英) As software scales become larger, the cost of testing also increases. To avoid the risk of
missing software defects during the testing phase and resulting serious consequences, machine
learning has been applied to software defect prediction (SDP) to assist in early identification of
defect modules. This enables testing resources to be focused on specific project modules,
allowing enterprises to produce higher-quality products at lower costs. In this study, the EEIPF (EasyEnsemble + Iterative-Partitioning Filter) architecture is combined with three different
oversampling methods, namely Polynom-fit-SMOTE, ProWsyn, and SMOTEIPF, to form the
Hybrid-EE-IPF structure for SDP. This study aims to alleviate the problem of data loss and
insufficient learning features caused by single random under-sampling in the EasyEnsemble
model and noisy data points in the SDP dataset. Unlike previous SDP studies that used a single
IPF filter to filter noisy data points, multiple filters are integrated with the ensemble model to
improve the diversity of base classifiers and enhance the prediction performance of software
defects.
關鍵字(中) ★ 軟體缺陷預測
★ 混合採樣
★ 集成學習
★ 迭代分層過濾器
★ 欠採樣
★ 過採樣
關鍵字(英) ★ Software Defect Prediction
★ Synthetic Sampling
★ Ensemble Learning
★ Iterative Partitioning Filter
★ Under-sampling
★ Over-sampling
論文目次 摘要............................................................................................................................i
致謝......................................................................................................................... iii
圖目錄.......................................................................................................................v
表目錄.....................................................................................................................vii
第一章 緒論.............................................................................................................1
1.1 研究背景 .........................................................................................................1
1.2 研究動機 .........................................................................................................2
1.3 研究目的 .........................................................................................................4
第二章 文獻探討 .....................................................................................................8
2.1 軟體缺陷預測分類的相關研究......................................................................8
2.2 類別不平衡問題...........................................................................................10
2.3 採樣技術(Sampling Methods).......................................................................10
第三章 研究方法 ...................................................................................................23
3.1 實驗資料集...................................................................................................24
3.2 模型評估方法...............................................................................................26
3.3 實驗流程 ......................................................................................................28
3.3.1 實驗一: 單一分類器使用......................................................................30
3.3.2 實驗 1.1~1.4 目的:..............................................................................33
3.3.3 實驗二: 集成式學習分類器使用..........................................................33
3.3.4 實驗 2.1~2.4 目的:..............................................................................38
第四章 實驗結果 ...................................................................................................39
4.1 實驗準備 ......................................................................................................39
4.2 實驗一: 單一分類器使用 ............................................................................40
4.2.1 子實驗 1.1: 單一採樣無搭配單一過濾器.............................................41
4.2.2 子實驗 1.2: 單一採樣搭配單一過濾器 ................................................42
應用混合式前處理與 IPF 過濾器之集成式學習於軟體缺陷預測
iv
4.2.3 子實驗 1.3: 混合採樣無搭配單一過濾器.............................................45
4.2.4 子實驗 1.4: 混合採樣搭配單一過濾器 ................................................48
4.3 實驗二: 集成分類器使用 ............................................................................53
4.3.1 子實驗 2.1: 單一採樣搭配集成式學習器.............................................53
4.3.2 子實驗 2.2: 單一採樣方式搭配 IPF 過濾器與集成式學習器..............55
4.3.3 子實驗 2.3: 混合採樣方式搭配集成式學習器.....................................57
4.3.4 子實驗 2.4: 混合採樣方式搭配 IPF 過濾器與集成式學習器..............59
第五章 實驗分析與驗證........................................................................................64
5.1 實驗一分析...................................................................................................64
5.2 實驗二分析...................................................................................................65
5.3 研究效度驗證...............................................................................................67
5.3.1 五折交叉驗證........................................................................................70
5.3.2 雙樣本中位數差異檢定(Wilcoxon signed-rank test) .............................71
第六章 結論與未來研究方向 ................................................................................76
6.1 研究貢獻 ......................................................................................................76
6.2 研究限制 ......................................................................................................78
6.3 未來研究方向................................................................................................78
參考文獻.................................................................................................................80
參考文獻 ALFRHAN, A. A., ALHUSAIN, R. H., & Khan, R. U. (2020, September). SMOTE:
Class imbalance problem in intrusion detection system. In 2020 International
Conference on Computing and Information Technology (ICCIT-1441) (pp. 1-5).
IEEE.
Alsawalqah, H., Faris, H., Aljarah, I., Alnemer, L., & Alhindawi, N. (2017). Hybrid
SMOTE-ensemble approach for software defect prediction. In Software
Engineering Trends and Techniques in Intelligent Systems: Proceedings of the
6th Computer Science On-line Conference 2017 (CSOC2017), Vol 3 6 (pp. 355-
366). Springer International Publishing.
Bagheri, E., & Gasevic, D. (2011). Assessing the maintainability of software product line
feature models using structural metrics. Software Quality Journal, 19, 579-612.
Barua, S., Islam, M. M., & Murase, K. (2013). ProWSyn: Proximity weighted synthetic
oversampling technique for imbalanced data set learning. In Advances in
Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD
2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II 17 (pp.
317-328). Springer Berlin Heidelberg.
Bashir, K., Ali, T., Yahaya, M., & Hussein, A. S. (2019, November). A hybrid data
preprocessing technique based on maximum likelihood logistic regression with
filtering for enhancing software defect prediction. In 2019 IEEE 14th
International Conference on Intelligent Systems and Knowledge Engineering
(ISKE) (pp. 921-927). IEEE.
Yohannese, C. W., & Mahama, Y. (2017, November). Enhancing
software defect prediction using supervised-learning based framework. In 2017
12th International Conference on Intelligent Systems and Knowledge
Engineering (ISKE) (pp. 1-6). IEEE.
Batista, G. E., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several
methods for balancing machine learning training data. ACM SIGKDD
explorations newsletter, 6(1), 20-29.
Bennin, K. E., Keung, J. W., & Monden, A. (2019). On the relative value of data
resampling approaches for software defect prediction. Empirical Software
Engineering, 24(2), 602-636.
Bennin, K. E., Keung, J., Phannachitta, P., Monden, A., & Mensah, S. (2017). Mahakil:
Diversity based oversampling approach to alleviate the class imbalance issue in
software defect prediction. IEEE Transactions on Software Engineering, 44(6),
534-550.
Broniatowski, D. A., & Tucker, C. (2017). Assessing causal claims about complex
engineered systems with quantitative data: internal, external, and construct
validity. Systems Engineering, 20(6), 483-496.
Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class
imbalance problem in convolutional neural networks. Neural networks, 106, 249-
259.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE:
synthetic minority over-sampling technique. Journal of artificial intelligence
research, 16, 321-357.
Chen, L., Fang, B., Shang, Z., & Tang, Y. (2018). Tackling class overlap and imbalance
problems in software defect prediction. Software Quality Journal, 26, 97-125.
Chen, S., Liu, M., Liu, T., & Li, J. (2021, March). Urban land use simulation based on
ProWSyn-MLP-CA. In IOP Conference Series: Earth and Environmental
Science (Vol. 692, No. 4, p. 042020). IOP Publishing.
Chen, Z., Yan, Q., Han, H., Wang, S., Peng, L., Wang, L., & Yang, B. (2018). Machine
learning based mobile malware detection using highly imbalanced network
traffic. Information Sciences, 433, 346-364.
Chen, X., Kang, Q., Zhou, M., & Wei, Z. (2016, August). A novel under-sampling
algorithm based on iterative-partitioning filters for imbalanced classification.
In 2016 IEEE International Conference on Automation Science and Engineering
(CASE) (pp. 490-494). IEEE.
Chen, X., Kang, Q., Zhou, M., & Wei, Z. (2016, August). A novel under-sampling
algorithm based on iterative-partitioning filters for imbalanced classification.
In 2016 IEEE International Conference on Automation Science and Engineering
(CASE) (pp. 490-494). IEEE.
Douzas, G., Bacao, F., & Last, F. (2018). Improving imbalanced learning through a
heuristic oversampling method based on k-means and SMOTE. Information
Sciences, 465, 1-20.
Elreedy, D., & Atiya, A. F. (2019). A comprehensive analysis of synthetic minority
oversampling technique (SMOTE) for handling class imbalance. Information
Sciences, 505, 32-64.
El-Shorbagy, S. A., El-Gammal, W. M., & Abdelmoez, W. M. (2018, May). Using
SMOTE and heterogeneous stacking in ensemble learning for software defect
prediction. In Proceedings of the 7th International Conference on Software and
Information Engineering (pp. 44-47).
Feng, S., Keung, J., Yu, X., Xiao, Y., & Zhang, M. (2021). Investigation on the stability
of SMOTE-based oversampling techniques in software defect
prediction. Information and Software Technology, 139, 106662.
Fernández, A., Garcia, S., Herrera, F., & Chawla, N. V. (2018). SMOTE for learning
from imbalanced data: progress and challenges, marking the 15-year
anniversary. Journal of artificial intelligence research, 61, 863-905.
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2011). A review
on ensembles for the class imbalance problem: bagging-, boosting-, and hybridbased approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C
(Applications and Reviews), 42(4), 463-484.
Garcia, L. P., Lehmann, J., de Carvalho, A. C., & Lorena, A. C. (2019). New label noise
injection methods for the evaluation of noise filters. Knowledge-Based
Systems, 163, 693-704.
Gazzah, S., & Amara, N. E. B. (2008, September). New oversampling approaches based
on Polynomial fitting for imbalanced data sets. In 2008 the eighth iapr
international workshop on document analysis systems (pp. 677-684). IEEE.
Ghotra, B., McIntosh, S., & Hassan, A. E. (2015, May). Revisiting the impact of
classification techniques on the performance of defect prediction models. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (Vol.
1, pp. 789-800). IEEE.
Gong, L., Jiang, S., & Jiang, L. (2019). Tackling class imbalance problem in software
defect prediction through cluster-based over-sampling with filtering. IEEE
Access, 7, 145725-145737.
Gondra, I. (2008). Applying machine learning to software fault-proneness
prediction. Journal of Systems and Software, 81(2), 186-195.
Goyal, S. (2022). Handling class-imbalance with KNN (neighbourhood) under-sampling
for software defect prediction. Artificial Intelligence Review, 55(3), 2023-2064.
Guo, Y., Jiang, X., Tao, L., Meng, L., Dai, C., Long, X., Chen, C. (2022). Epileptic
seizure detection by cascading isolation forest-based anomaly screening and
EasyEnsemble. IEEE Transactions on Neural Systems and Rehabilitation
Engineering, 30, 915-924.
Hall, T., & Bowes, D. (2012, December). The state of machine learning methodology in
software fault prediction. In 2012 11th international conference on machine
learning and applications (Vol. 2, pp. 308-313). IEEE
Han, M., Guo, H., Li, J., & Wang, W. (2022). Global-local information based
oversampling for multi-class imbalanced data. International Journal of Machine
Learning and Cybernetics, 1-16.
Han, W., Huang, Z., Li, S., & Jia, Y. (2019). Distribution-sensitive unbalanced data
oversampling method for medical diagnosis. Journal of medical Systems, 43, 1-
10.
Huda, S., Liu, K., Abdelrazek, M., Ibrahim, A., Alyahya, S., Al-Dossari, H., & Ahmad,
S. (2018). An ensemble oversampling model for class imbalance problem in
software defect prediction. IEEE access, 6, 24184-24195.
Iqbal, A., Aftab, S., Ali, U., Nawaz, Z., Sana, L., Ahmad, M., & Husen, A. (2019).
Performance analysis of machine learning techniques on software defect
prediction using NASA datasets. International Journal of Advanced Computer
Science and Applications, 10(5).
Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class
imbalance. Journal of Big Data, 6(1), 1-54.
Kang, Q., Chen, X., Li, S., & Zhou, M. (2016). A noise-filtered under-sampling scheme
for imbalanced classification. IEEE transactions on cybernetics, 47(12), 4263-
4274.
Kaur, H., Pannu, H. S., & Malhi, A. K. (2019). A systematic review on imbalanced data
challenges in machine learning: Applications and solutions. ACM Computing
Surveys (CSUR), 52(4), 1-36.
Kitchenham, B., Pickard, L., & Pfleeger, S. L. (1995). Case studies for method and tool
evaluation. IEEE software, 12(4), 52-6
Kovács, G. (2019). An empirical comparison and evaluation of minority oversampling
techniques on a large number of imbalanced datasets. Applied Soft
Computing, 83, 105662.
Laradji, I. H., Alshayeb, M., & Ghouti, L. (2015). Software defect prediction using
ensemble learning on selected features. Information and Software
Technology, 58, 388-402.
Leevy, J. L., Khoshgoftaar, T. M., Bauder, R. A., & Seliya, N. (2018). A survey on
addressing high-class imbalance in big data. Journal of Big Data, 5(1), 1-30.
Liang, J., Bai, L., Dang, C., & Cao, F. (2012). The K-means-type algorithms versus
imbalanced data distributions. IEEE Transactions on Fuzzy Systems, 20(4), 728-
745.
Lin, C., Tsai, C. F., & Lin, W. C. (2023). Towards hybrid over-and under-sampling
combination methods for class imbalanced datasets: an experimental
study. Artificial Intelligence Review, 56(2), 845-863.
Lingden, P., Alsadoon, A., Prasad, P. W. C., Alsadoon, O. H., Ali, R. S., & Nguyen, V.
T. Q. (2019). A novel modified undersampling (MUS) technique for software
defect prediction. Computational Intelligence, 35(4), 1003-1020.
Lin, W. C., Tsai, C. F., Hu, Y. H., & Jhang, J. S. (2017). Clustering-based
undersampling in class-imbalanced data. Information Sciences, 409, 17-26.
Liu, T. (2016, November). Fault diagnosis of gearbox by selective ensemble learning
based on artificial immune algorithm. In 2016 3rd International Conference on
Systems and Informatics (ICSAI) (pp. 460-464). IEEE.
Malhotra, R. (2016). Empirical research in software engineering: concepts, analysis,
and applications. Book, Chapman and Hall/CRC press.
Malhotra, R. (2015). A systematic review of machine learning techniques for software
fault prediction. Applied Soft Computing, 27, 504-518.
Odejide, B. J., Bajeh, A. O., Balogun, A. O., Alanamu, Z. O., Adewole, K. S., Akintola,
A. G., Mojeed, H. A. (2022). An Empirical Study on Data Sampling Methods in
Addressing Class Imbalance Problem in Software Defect Prediction. In Computer
Science On-line Conference (pp. 594-610). Springer, Cham.
Pelayo, L., & Dick, S. (2012). Evaluating stratification alternatives to improve software
defect prediction. IEEE transactions on reliability, 61(2), 516-525.
Qazi, A. W., Saqib, Z., & Zaman-ul-Haq, M. (2022). Trends in species distribution
modelling in context of rare and endemic plants: A systematic review. Ecological
Processes, 11(1), 1-11.
Radjenović, D., Heričko, M., Torkar, R., & Živkovič, A. (2013). Software fault
prediction metrics: A systematic literature review. Information and software
technology, 55(8), 1397-1418.
Rao, K. N., & Reddy, C. S. (2020). A novel under sampling strategy for efficient
software defect analysis of skewed distributed data. Evolving Systems, 11, 119-
131.
Rathore, S. S., & Kumar, S. (2017). A decision tree logic based recommendation system
to select software fault prediction techniques. Computing, 99, 255-285.
Reza, M. S., & Ma, J. (2018, August). Imbalanced histopathological breast cancer image
classification with convolutional neural network. In 2018 14th IEEE
International Conference on Signal Processing (ICSP) (pp. 619-624). IEEE.
Rhmann, W., Pandey, B., Ansari, G., & Pandey, D. K. (2020). Software fault prediction
based on change metrics using hybrid algorithms: An empirical study. Journal of
King Saud University-Computer and Information Sciences, 32(4), 419-424.
Riaz, S., Arshad, A., & Jiao, L. (2018). Rough noise-filtered easy ensemble for software
fault prediction. Ieee Access, 6, 46886-46899.
Robinson, O. J., Ruiz‐Gutierrez, V., & Fink, D. (2018). Correcting for bias in
distribution modelling for rare species using citizen science data. Diversity and
Distributions, 24(4), 460-472.
Sáez, J. A., Galar, M., Luengo, J., & Herrera, F. (2016). INFFC: An iterative class noise
filter based on the fusion of classifiers with noise sensitivity control. Information
Fusion, 27, 19-32.
Sáez, J. A., Luengo, J., Stefanowski, J., & Herrera, F. (2015). SMOTE–IPF: Addressing
the noisy and borderline examples problem in imbalanced classification by a resampling method with filtering. Information Sciences, 291, 184-203.
Sáez, J. A., Galar, M., Luengo, J., & Herrera, F. (2013). Tackling the problem of
classification with noisy data using multiple classifier systems: Analysis of the
performance and robustness. Information Sciences, 247, 1-20
Sanjaya, S., Abdillah, R., & Afrianty, I. (2022, October). The impact of Under-Sampling
Techniques on Classification Accuracy in multi-class Imbalance Data. In 2022
3rd International Conference on Electrical Engineering and Informatics (ICon
EEI) (pp. 92-97). IEEE.
Santos, M. S., Soares, J. P., Abreu, P. H., Araujo, H., & Santos, J. (2018). Crossvalidation for imbalanced datasets: avoiding overoptimistic and overfitting
approaches [research frontier]. ieee ComputatioNal iNtelligeNCe
magaziNe, 13(4), 59-76.
Seiffert, C., Khoshgoftaar, T. M., Van Hulse, J., & Folleco, A. (2014). An empirical
study of the classification performance of learners on imbalanced and noisy
software quality data. Information Sciences, 259, 571-595
Shin, J., Yoon, S., Kim, Y., Kim, T., Go, B., & Cha, Y. (2021). Effects of class
imbalance on resampling and ensemble learning for improved prediction of
cyanobacteria blooms. Ecological informatics, 61, 101202.
Soe, Y. N., Santosa, P. I., & Hartanto, R. (2019, October). Ddos attack detection based
on simple ann with smote for iot environment. In 2019 fourth international
conference on informatics and computing (ICIC) (pp. 1-5). IEEE.
Trochim, W. M., & Donnelly, J. P. (2001). Research methods knowledge base (Vol. 2).
Macmillan Publishing Company, New York: Atomic Dog Pub.
Tsai, C. F., Lin, W. C., Hu, Y. H., & Yao, G. T. (2019). Under-sampling class
imbalanced datasets by combining clustering analysis and instance
selection. Information Sciences, 477, 47-54.
Van Hulse, J., Khoshgoftaar, T. M., & Napolitano, A. (2010, December). A novel noise
filtering algorithm for imbalanced data. In 2010 Ninth International Conference
on Machine Learning and Applications (pp. 9-14). IEEE.
Vuttipittayamongkol, P., & Elyan, E. (2020). Neighbourhood-based undersampling
approach for handling imbalanced and overlapped data. Information
Sciences, 509, 47-70.
Wahab, N., Khan, A., & Lee, Y. S. (2017). Two-phase deep convolutional neural
network for reducing class skewness in histopathological images based breast
cancer detection. Computers in biology and medicine, 85, 86-97.
Wahono, R. S. (2015). A systematic literature review of software defect
prediction. Journal of software engineering, 1(1), 1-16.
u, W., & Liu, C. (2022). Imbalanced heartbeat classification using
EasyEnsemble technique and global heartbeat information. Biomedical Signal
Processing and Control, 71, 103105.
Wang, Y., Pan, Z., Zheng, J., Qian, L., & Li, M. (2019). A hybrid ensemble method for
pulsar candidate classification. Astrophysics and Space Science, 364, 1-13.
Wang, S., & Yao, X. (2013). Using class imbalance learning for software defect
prediction. IEEE Transactions on Reliability, 62(2), 434-443.
Wang, S., Li, Z., Chao, W., & Cao, Q. (2012, June). Applying adaptive over-sampling
technique based on data density and cost-sensitive SVM to imbalanced learning.
In The 2012 international joint conference on neural networks (IJCNN) (pp. 1-8).
IEEE.
Wardhani, N. W. S., Rochayani, M. Y., Iriany, A., Sulistyono, A. D., & Lestantyo, P.
(2019, October). Cross-validation metrics for evaluating classification
performance on imbalanced data. In 2019 International conference on computer,
control, informatics and its applications (IC3INA) (pp. 14-18). IEEE.
Wu, Z., Lin, W., & Ji, Y. (2018). An integrated ensemble learning model for imbalanced
fault diagnostics and prognostics. IEEE Access, 6, 8394-8402.
Xu, Z., Shen, D., Nie, T., & Kou, Y. (2020). A hybrid sampling algorithm combining MSMOTE and ENN based on random forest for medical imbalanced data. Journal
of Biomedical Informatics, 107, 103465.
Yen, S. J., & Lee, Y. S. (2009). Cluster-based under-sampling approaches for
imbalanced data distributions. Expert Systems with Applications, 36(3), 5718-
5727.
Zhang, C., Tan, K. C., Li, H., & Hong, G. S. (2018). A cost-sensitive deep belief
network for imbalanced classification. IEEE transactions on neural networks and
learning systems, 30(1), 109-122.
Zhang, H., Huang, L., Wu, C. Q., & Li, Z. (2020). An effective convolutional neural
network based on SMOTE and Gaussian mixture model for intrusion detection in
imbalanced dataset. Computer Networks, 177, 107315.
Zhu, Y., Yan, Y., Zhang, Y., & Zhang, Y. (2020). EHSO: Evolutionary Hybrid Sampling
in overlapping scenarios for imbalanced learning. Neurocomputing, 417, 333-
346.
指導教授 陳仲儼(Chung-Yen Chen) 審核日期 2023-7-11
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