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: 60PDF 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
- Benjamin KOMMEY, Ernest Ofosu ADDO, Elvis TAMAKLOE, Eric Tutu TCHAO, Henry NUNOO-MENSAH, A SIX-PORT MEASUREMENT DEVICE FOR HIGH POWER MICROWAVE VECTOR NETWORK ANALYSIS , Applied Computer Science: Vol. 18 No. 3 (2022)
- 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)
- Workineh TESEMA, INEFFICIENCY OF DATA MINING ALGORITHMS AND ITS ARCHITECTURE: WITH EMPHASIS TO THE SHORTCOMING OF DATA MINING ALGORITHMS ON THE OUTPUT OF THE RESEARCHES , Applied Computer Science: Vol. 15 No. 3 (2019)
- Victor CHUNG, Jenny ESPINOZA, A LATIN AMERICAN MARKET ASSET VOLATILITY ANALYSIS: A COMPARISON OF GARCH MODEL, ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR REGRESSION , Applied Computer Science: Vol. 19 No. 3 (2023)
- Ghania Zidani, Djalal DJARAH, Abdslam BENMAKHLOUF, Laid KHETTACHE, OPTIMIZING PEDESTRIAN TRACKING FOR ROBUST PERCEPTION WITH YOLOv8 AND DEEPSORT , Applied Computer Science: Vol. 20 No. 1 (2024)
- Marcin Topczak, Małgorzata Śliwa, ASSESSMENT OF THE POSSIBILITY OF USING BAYESIAN NETS AND PETRI NETS IN THE PROCESS OF SELECTING ADDITIVE MANUFACTURING TECHNOLOGY IN A MANUFACTURING COMPANY , Applied Computer Science: Vol. 17 No. 1 (2021)
- Nouhaila BOUALOULOU, Taoufiq BELHOUSSINE DRISSI, Benayad NSIRI, CNN AND LSTM FOR THE CLASSIFICATION OF PARKINSON'S DISEASE BASED ON THE GTCC AND MFCC , Applied Computer Science: Vol. 19 No. 2 (2023)
- Wieslaw FRĄCZ, Grzegorz JANOWSKI, INFLUENCE OF HOMOGENIZATION METHODS IN PREDICTION OF STRENGTH PROPERTIES FOR WPC COMPOSITES , Applied Computer Science: Vol. 13 No. 3 (2017)
- Olufemi Folorunso, Olufemi Akinyede, Kehinde Agbele, ARDP: SIMPLIFIED MACHINE LEARNING PREDICTOR FOR MISSING UNIDIMENSIONAL ACADEMIC RESULTS DATASET , Applied Computer Science: Vol. 19 No. 1 (2023)
- Archana Gunakala, Afzal Hussain Shahid, A COMPARATIVE STUDY ON PERFORMANCE OF BASIC AND ENSEMBLE CLASSIFIERS WITH VARIOUS DATASETS , Applied Computer Science: Vol. 19 No. 1 (2023)
You may also start an advanced similarity search for this article.