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: 58PDF downloads: 1
License
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
- Manikandan SRIDHARAN, Delphin Carolina RANI ARULANANDAM, Rajeswari K CHINNASAMY, Suma THIMMANNA, Sivabalaselvamani DHANDAPANI, RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING , Applied Computer Science: Vol. 17 No. 2 (2021)
- Nataliya SHABLIY, Serhii LUPENKO, Nadiia LUTSYK, Oleh YASNIY, Olha MALYSHEVSKA, KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 17 No. 4 (2021)
- Robert KARPIŃSKI, Jakub GAJEWSKI, Jakub SZABELSKI, Dalibor BARTA, APPLICATION OF NEURAL NETWORKS IN PREDICTION OF TENSILE STRENGTH OF ABSORBABLE SUTURES , Applied Computer Science: Vol. 13 No. 4 (2017)
- Edyta ŁUKASIK, Wiktor FLIS, EFFICIENCY COMPARISON OF NETWORKS IN HANDWRITTEN LATIN CHARACTERS RECOGNITION WITH DIACRITICS , Applied Computer Science: Vol. 19 No. 4 (2023)
- Monika KULISZ, Justyna KUJAWSKA, Zulfiya AUBAKIROVA, Gulnaz ZHAIRBAEVA, Tomasz WAROWNY, PREDICTION OF THE COMPRESSIVE STRENGTH OF ENVIRONMENTALLY FRIENDLY CONCRETE USING ARTIFICIAL NEURAL NETWORK , Applied Computer Science: Vol. 18 No. 4 (2022)
- Wulan Dewi, Wiranto Herry Utomo, PLANT CLASSIFICATION BASED ON LEAF EDGES AND LEAF MORPHOLOGICAL VEINS USING WAVELET CONVOLUTIONAL NEURAL NETWORK , Applied Computer Science: Vol. 17 No. 1 (2021)
- Rowell HERNANDEZ, Robert ATIENZA, CAREER TRACK PREDICTION USING DEEP LEARNING MODEL BASED ON DISCRETE SERIES OF QUANTITATIVE CLASSIFICATION , Applied Computer Science: Vol. 17 No. 4 (2021)
- Puppala Praneeth, Majety Sathvika, Vivek Kommareddy, Madala Sarath, Saran Mallela, Koneru Suvarna Vani, Prasun Chkrabarti, CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK , Applied Computer Science: Vol. 19 No. 2 (2023)
- Nawazish NAVEED, Hayan T. MADHLOOM, Mohd Shahid HUSAIN, BREAST CANCER DIAGNOSIS USING WRAPPER-BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORK , Applied Computer Science: Vol. 17 No. 3 (2021)
- Md. Torikur RAHMAN, A NOVEL APPROACH TO ENHANCE THE PERFORMANCE OF MOBILE AD HOC NETWORK (MANET) THROUGH A NEW BANDWIDTH OPTIMIZATION TECHNIQUE , Applied Computer Science: Vol. 15 No. 2 (2019)
You may also start an advanced similarity search for this article.
