A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION
Saleh ALBAHLI
salbahli@qu.edu.saQassim University, College of Computer, Department of Information Technology (Saudi Arabia)
Abstract
Nowadays, logistics for transportation and distribution of merchandise are a key element to increase the competitiveness of companies. However, the election of alternative routes outside the panned routes causes the logistic companies to provide a poor-quality service, with units that endanger the appropriate deliver of merchandise and impacting negatively the way in which the supply chain works. This paper aims to develop a module that allows the processing, analysis and deployment of satellite information oriented to the pattern analysis, to find anomalies in the paths of the operators by implementing the algorithm TODS, to be able to help in the decision making. The experimental results show that the algorithm detects optimally the abnormal routes using historical data as a base.
Keywords:
Deep Neural Network, unlabeled dataset, Just-In-Time defect prediction, unsupervised predictionReferences
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794). ACM. https://doi.org/10.1145/2939672.2939785
DOI: https://doi.org/10.1145/2939672.2939785
Google Scholar
Hata, H., Mizuno, O., & Kikuno, T. (2012). Bug prediction based on fine-grained module histories. In Proceedings of the 34th International Conference on Software Engineering (pp. 200–210). IEEE Press.
DOI: https://doi.org/10.1109/ICSE.2012.6227193
Google Scholar
Huang, Q., Xia, X., & Lo, D. (2019). Revisiting supervised and unsupervised models for effortaware just-in-time defect prediction. Empirical Software Engineering, 24(5), 2823–2862. https://doi.org/10.1007/s10664-018-9661-2
DOI: https://doi.org/10.1007/s10664-018-9661-2
Google Scholar
Kamei, Y., Matsumoto, S., Monden, A., Matsumoto, K.I., Adams, B., & Hassan, A. E. (2010). Revisiting common bug prediction findings using effort-aware models. In 2010 IEEE International Conference on Software Maintenance (pp. 1–10). IEEE. https://doi.org/10.1109/ICSM.2010.5609530
DOI: https://doi.org/10.1109/ICSM.2010.5609530
Google Scholar
Kamei, Y., Shihab, E., Adams, B., Hassan, A.E., Mockus, A., Sinha, A., & Ubayashi, N. (2012). A large-scale empirical study of just-in-time quality assurance. IEEE Transactions on Software Engineering, 39(6), 57–773. http://doi.org/10.1109/TSE.2012.70
DOI: https://doi.org/10.1109/TSE.2012.70
Google Scholar
Liu, C., Yang, D., Xia, X., Yan, M., & Zhang, X. (2018). Cross-Project Change-Proneness Prediction. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) (Vol. 1, pp. 64–73). IEEE.
DOI: https://doi.org/10.1109/COMPSAC.2018.00017
Google Scholar
Mockus, A., & Weiss, D.M. (2000). Predicting risk of software changes. Bell Labs Technical Journal, 5(2), 169–180.
DOI: https://doi.org/10.1002/bltj.2229
Google Scholar
Qiao, L., & Wang, Y. (2019). Effort-aware and just-in-time defect prediction with neural network. PloS one, 14(2), e0211359. https://doi.org/10.1371/journal.pone.0211359
DOI: https://doi.org/10.1371/journal.pone.0211359
Google Scholar
Yang, Y., Zhou, Y., Liu, J., Zhao, Y., Lu, H., Xu, L., ... & Leung, H. (2016). Effort-aware just-intime defect prediction: simple unsupervised models could be better than supervised models. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (pp. 157–168). ACM. https://doi.org/10.1145/2950290.2950353
DOI: https://doi.org/10.1145/2950290.2950353
Google Scholar
Yu, T., Wen, W., Han, X., & Hayes, J. (2018). ConPredictor: Concurrency Defect Prediction in Real-World Applications. IEEE Transactions on Software Engineering, 45(6), 558–575. https://doi.org/10.1109/TSE.2018.2791521
DOI: https://doi.org/10.1109/TSE.2018.2791521
Google Scholar
Zhou, T., Sun, X., Xia, X., Li, B., & Chen, X. (2019). Improving defect prediction with deep forest. Information and Software Technology, 114, 204–216. https://doi.org/10.1016/j.infsof.2019.07.003
DOI: https://doi.org/10.1016/j.infsof.2019.07.003
Google Scholar
Authors
Saleh ALBAHLIsalbahli@qu.edu.sa
Qassim University, College of Computer, Department of Information Technology Saudi Arabia
Statistics
Abstract views: 85PDF downloads: 10
License
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Elmehdi BENMALEK, Jamal EL MHAMDI, Abdelilah JILBAB, Atman JBARI, A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS , Applied Computer Science: Vol. 18 No. 4 (2022)
- Anna MACHROWSKA, Robert KARPIŃSKI, Józef JONAK, Jakub SZABELSKI, NUMERICAL PREDICTION OF THE COMPONENT-RATIO-DEPENDENT COMPRESSIVE STRENGTH OF BONE CEMENT , Applied Computer Science: Vol. 16 No. 3 (2020)
- Anna MACHROWSKA, Robert KARPIŃSKI, Marcin MACIEJEWSKI, Józef JONAK, Przemysław KRAKOWSKI, APPLICATION OF EEMD-DFA ALGORITHMS AND ANN CLASSIFICATION FOR DETECTION OF KNEE OSTEOARTHRITIS USING VIBROARTHROGRAPHY , Applied Computer Science: Vol. 20 No. 2 (2024)
- Saheed A. ADEWUYI, Segun AINA, Adeniran I. OLUWARANTI, A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA , Applied Computer Science: Vol. 16 No. 1 (2020)
- Marcin TOMCZYK, Barbara BOROWIK, Mariusz MIKULSKI, IDENTIFICATION OF A BACKLASH ZONE IN AN ELECTROMECHANICAL SYSTEM CONTAINING CHANGES OF A MASS INERTIA MOMENT BASED ON A WAVELET–NEURAL METHOD , Applied Computer Science: Vol. 14 No. 4 (2018)
- Grzegorz KŁOSOWSKI, Tomasz KLEPKA, Agnieszka NOWACKA, NEURAL CONTROLLER FOR THE SELECTION OF RECYCLED COMPONENTS IN POLYMER-GYPSY MORTARS , Applied Computer Science: Vol. 14 No. 2 (2018)
- Krzysztof OSTROWSKI, AN EFFECTIVE METAHEURISTIC FOR TOURIST TRIP PLANNING IN PUBLIC TRANSPORT NETWORKS , Applied Computer Science: Vol. 14 No. 2 (2018)
- Erizal ERIZAL, Mohammad DIQI, PERFORMANCE EVALUATION OF STOCK PREDICTION MODELS USING EMAGRU , Applied Computer Science: Vol. 19 No. 3 (2023)
- Lukas BAUER, Leon STÜTZ, Markus KLEY, BLACK BOX EFFICIENCY MODELLING OF AN ELECTRIC DRIVE UNIT UTILIZING METHODS OF MACHINE LEARNING , Applied Computer Science: Vol. 17 No. 4 (2021)
- Pannangi Naresh, R. Suguna, IMPLEMENTATION OF DYNAMIC AND FAST MINING ALGORITHMS ON INCREMENTAL DATASETS TO DISCOVER QUALITATIVE RULES , Applied Computer Science: Vol. 17 No. 3 (2021)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.