PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS

raghdaazad hassan; Ibrahim Ahmed  Saleh

doi:10.30572/2018/KJE/160336

Authors

raghdaazad hassan MSC student, Software Department, Faculty of Computer Science and Mathematics, University of Mosul, Nineveh, Iraq
Ibrahim Ahmed Saleh Department of Software, Faculty of Computer Science and Mathematics, University of Mosul, Nineveh, Iraq

DOI:

https://doi.org/10.30572/2018/KJE/160336

Keywords:

Software Engineering, Software Defect Prediction, Ensemble Learning, Random Forest, Decision Tree, Boosting, Stacking

Abstract

Software plays a vital role in all aspects of our daily lives, specifically in the fields of medicine and industry. In order to design high-quality and reliable software and avoid risks resulting from software errors, including physical and human errors, this is considered a major challenge due to the limited time and budget specified. Therefore, most software development companies tend to use machine learning for prediction. With the presence of software defects that contribute to improving the quality and safety of the software produced, this is done by relying on and using records, previous projects, and available data. this paper proposed machine learning and ensemble learning suite to predict software anomalies. The evaluated approach is for models in the PROMISE real-word dataset repository containing 5 projects (Turkish company SOFTLAB). The model applies the basic algorithms (Random Forest (RF), Decision Tree (DT), Extra Tree) and the learning model ensemble (Adaboost, xgboost ,Stack, Voting, bagging) and metrics (accuracy, recall, F1 score, accuracy) to measure the prediction performance of the models and a comparison was made between the proposed model algorithms. Both adaboost , stack achieved prediction accuracy about 99.2% when implemented on the ar5 dataset.

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References

Abbas, S. et al ,( 2023), “Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System”, http://dx.doi.org/10.32604/cmc.2023.037933

Abdou, A. S, and Darwish , N. R, (2018),“Early Prediction of Software Defect using Ensemble Learning: A Comparative Study”, International Journal of Computer Applications (0975 – 8887) Volume 179 – No.46.

AL-FRAIHAT, D. et al, (2024), “Hyperparameter Optimization for Software Bug Prediction Using Ensemble Learning”, Digital Object Identifier 10.1109/ACCESS.2024.3380024

Ali , U. et al, “Software Defect Prediction Using Variant based Ensemble Learning and Feature Selection Techniques”, Published Online October (2020) in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijmecs.2020.05.03

Balogun, A. et al , (2018), “Software Defect Prediction Using Ensemble Learning: An ANP Based Evaluation Method”, http://dx.doi.org/10.46792/fuoyejet.v3i2.200

Balogun, A.. et al , (2020), “SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction”, O. Gervasi et al. (Eds.): ICCSA 2020, LNCS 12254, pp. 615–631. https://doi.org/10.1007/978-3-030-58817-5_45

Burkov, Andriy. (2019). “The hundred-page machine learning” book. Vol. 1. Quebec City, QC, Canada: Andriy Burkov, https://doi.org/10.1080/15228053.2020.1766224

Elife Ozturk et al, (2021), “Multi-view learning for software defect prediction” , e-Informatica Software Engineering Journal , Volume 15, Issue 1.

Ernest Kwame Ampomah et al , (2020),“Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement”, http://dx.doi.org/10.3390/info11060332

Hersh A. Mohammed et al, (2020), “A COMPARATIVE EVALUATION OF DEEP LEARNING METHODS IN DIGITAL IMAGE CLASSIFICATION” , Kufa Journal of Engineering Vol. 13, No. 4, October 2022, P.P. 53-69

Huda, S. et al , (2021),“An ensemble oversampling model for class imbalance problem in software defect prediction”, Citation information: DOI 10.1109/ACCESS.2018.2817572

Jiayao Chen et al, (2021),“Machine learning-based classification of rock discontinuity trace: SMOTE oversampling integrated with GBT ensemble learning”, https://doi.org/10.1016/j.ijmst.2021.08.004

Jun-hai Zhai et al, (2012) ,“Dynamic ensemble extreme learning machine based on sample entropy”, Soft Comput 16:1493–1502,

Khuat, T. T. and Le , M. , (2019), “Ensemble learning for software fault prediction problem with imbalanced data”, Vol. 9, No. 4, pp. 3241~3246 ISSN: 2088-8708, DOI: 10.11591/ijece.v9i4.pp3241-3246

Matloob, F. et al , (2019),“A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques”, Published Online December 2019 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijmecs 12.02.

MISBAH ALI et al , (2024),“Software Defect Prediction Using an Intelligent Ensemble-Based Model”, VOLUME 12, https://creativecommons.org/licenses/by-nc-nd/4.0/

Mohammad Amimul Ihsan Aquil, Wan Hussain Wan Ishak, (2020), “Predicting Software Defects using Machine Learning Techniques”, https://doi.org/10.30534/ijatcse/2020/352942020

Monni, C. , and Pezze`, M. , (2019), “Energy-Based Anomaly Detection A New Perspective for Predicting Software Failures”, http://dx.doi.org/10.13140/RG.2.2.29124.88967

Q. Song, Z. Jia, M. Shepperd, S. Ying, and J. Liu. (2010),A general software defect-proneness prediction framework. IEEE transactions on software engineering, 37(3), 356-370,

R. Mamatha et al, (2023),“Enhanced Software Defect Prediction Through Homogeneous Ensemble Models”.

REN AND LIU, (2019), “Predicting Software Defects Using Self-Organizing Data Mining” , Digital Object Identifier 10.1109/ACCESS.2019.2927489.

Sagheer Abbas et al, (2023), “Bata and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System”, http://dx.doi.org/10.32604/cmc.2023.037933

Saheed , Y. K. et al, (2021),“An Ensemble Learning Approach for Software Defect Prediction in Developing Quality Software Product”, https://www.researchgate.net/publication/355490443_An_Ensemble_Learning_Approach_for_Software_Defect_Prediction_in_Developing_Quality_Software_Product?enrichId=rgreq-732adb78eca05ef05a16767ff9755563-XXX&enrichSource=Y292ZXJQYWdlOzM1NTQ5MDQ0MztBUzoxMTA0MDg4MjMwMDQzNjQ4QDE2NDAyNDY1NTUwMDc%3D&el=1_x_2&_esc=publicationCoverPdf

Sharm, T. et al, (2023),“Ensemble Machine Learning Paradigms in Software Defect Prediction”, Available online at www.sciencedirect.com Procedia Computer Science 218 199–209

Shi ,T. , Zou , Z. and Ai , J., (2023),“Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model”, Appl. Sci. 13, 11349. https://doi.org/10.3390/app132011349

Shuo Feng, et al. (2021),“Investigation on the stability of SMOTE-based oversampling techniques in software defect prediction”. https://doi.org/10.1016/j.infsof.2021.106662

Sivalingan H , (2024),“Cloud-Smart Surveillance: Enhancing Anomaly Detection In Video Streams With DfConvlstm-Based Vae-Ga" , Kufa Journal of Engineering Vol. 15, No. 4, October 2024, P.P. 125-140.

Xibin DONG et al , (2020),“A survey on ensemble learning”, Front. Comput. Sci., 14(2): 241–258 https://doi.org/10.1007/s11704-019-8208-z

Xin, J. et al, (2023),“Ensemble learning based defect detection of laser sintering”, https://doi.org/10.1049/ote2.12108

Xin-She Yang, (2019),“Introduction to Algorithms for Data Mining and Machine Learning” [book], https://www.elsevier.com/books-and-journals

Yan-yan SONGand Ying LU, (2015),“Decision tree methods: applications for classification and prediction”, Shanghai Archives of Psychiatry, Vol. 27, No. 2 http://dx.doi.org/10.11919/j.issn.1002-0829.215044

YI PENG et al , (2011) ,“ENSEMBLE OF SOFTWARE DEFECT PREDICTORS: AN AHP-BASED EVALUATION METHOD”, International Journal of Information Technology & Decision Making Vol. 10, No. 1 187–206.

Zhao, Z. , (2017), “Ensemble Methods for Anomaly Detection”, Dissertations - ALL. 817. https://surface.syr.edu/etd/817