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Articles

Deep mining of open source software bug repositories

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Pages 614-622 | Received 30 Oct 2019, Accepted 19 Nov 2020, Published online: 15 Dec 2020

References

  • Yang G, Zhang T, Lee B. Towards semi-automatic bug triage and severity prediction based on topic model and multi-feature of bug reports. In Proceedings of the IEEE 38th Annual Computer Software and Applications Conference, COMPSAC’14; 2014. p. 97–106.
  • Xuan J, Jiang H, Hu Y, et al. Towards effective bug triage with software data reduction techniques. IEEE Trans Knowl Data Eng. January 2015;27(1):264–280.
  • Uddin J, Ghazali R, Mat Deris M, et al. A survey on bug prioritization. Artif Intell Rev. April 2016;47(2):145–180.
  • Xia X, Lo D, Wen M, et al. An empirical study of bug report field reassignment. In the Proceedings of the 2014 Software Evolution Week-IEEE Conference on Software Maintenance, Reengineering and Reverse Engineering, CSMR-WCRE’14; 2014. p. 174–183.
  • Menzies T, Marcus A. Automated severity assessment of software defect reports, In the Proceeding of IEEE International Conference on Software Maintenance ICSM 2008, Sept 2008. p. 346–355.
  • Lamkanfi A, Demeyer S, Giger E, et al. Predicting the severity of a reported bug. In the Proceedings of the 7th IEEE Working Conference on Mining Software Repositories, MSR’10; 2010. pp. 1–10.
  • Lamkanfi A, Demeyer S, Soetens QD, et al. Comparing mining algorithms for predicting the severity of a reported bug, In the proceedings of 15th European Conference on Software Maintenance and Reengineering (CSMR); 2011. p. 249–258.
  • Chaturvedi K, Singh V. Determining bug severity using machine learning techniques. In the proceedings of Sixth International Conference on Software Engineering (CONSEG); 2012. p. 1–6.
  • Yang CZ, Hou CC, Kao WC, et al. An empirical study on improving severity prediction of defect reports using feature selection. In the Proceedings of the 19th Asia-Pacific Software Engineering Conference, APSEC’12; 2012. p. 240–249.
  • Sharma G, Sharma S, Gujral S. A novel way of assessing software bug severity using dictionary of critical terms, In the Proceedings of 4th International Conference on Eco-friendly Computing and Communication Systems, ICECCS, 2015, Procedia Computer Science; 2015. p. 632–639.
  • Roy NKS, Rossi B. Towards an improvement of bug severity classification. 40th EUROMICRO Conference on Software Engineering and Advanced Applications; 2014, Verona, Italy.
  • Tian Y, Lo D, Sun C. Information retrieval based nearest neighbor classification for fine-grained bug severity prediction. In the proceedings of 19th Working Conference on Reverse Engineering (WCRE); 2012. p. 215–224.
  • Zhang a T, Chen J, Yang G, et al. Towards more accurate severity prediction and fixer recommendation of software bugs. J Syst Softw. 2016;117:166–184.
  • Hamdy A, El-laithy A. Using smote and feature reduction for more effective bug severity prediction. Int J Softw Eng Knowl Eng. 2019;29(6):897–919.
  • Hamdy A, El-Laithy A. Semantic categorization of software Bug repositories for severity assignment automation, integrating research and practice in software engineering. Stud Comput Intell. 2020;851:15–30.
  • Pushpalatha MN, Mrunalini M. Predicting the severity of open source Bug reports using Unsupervised and supervised techniques. Int J Open Source Softw Process. 2019;10(1):1–15.
  • Guo S, Chen R, Li H, et al. Identify severity Bug report with distribution imbalance by CR-SMOTE and ELM. Int J Softw Eng Knowl Eng. 2019;29(2):139–175.
  • Hotho A, Nurnberger A, Paas G. A brief survey of text mining. J Comput Linguist Lang Technol. 2005;20:19–62.
  • Hamdy A, Elsayed M. Automatic recommendation of software design patterns: text retrieval approach. J Softw. April 2018;13(4):260–268.
  • Hamdy A, Elsayed M. Towards more accurate automatic recommendation of software design patterns. J Theor Appl Inf Technol. 2018;96(15):5069–5079.
  • Hamdy A, Elsayed M. Topic modelling for automatic selection of software design patterns. In Proceedings of the International Conference on Geoinformatics and Data Analysis ICGDA ‘18, Prague, Czech Republic, April 20th–22nd; 2018. p. 41–46.
  • Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst. 2013;26:3111–3119.
  • Wang D, Zhao K, Wang Y. Based on deep learning in traffic remote sensing image processing to recognize target vehicle. Int J Comput Appl. 2020;42:1–7.
  • Haritha H, Thangavel SK. A modified deep learning architecture for vehicle detection in traffic monitoring system. Int J Comput Appl. 2019;41.
  • Suriya M, Chandran V, Sumithra MG. Enhanced deep convolutional neural network for malarial parasite classification. Int J Comput Appl. 2019;41.
  • Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–1780.
  • Sundermeyer M, Schl¨uter R, Ney H. LSTM neural networks for language modeling. In Thirteenth Annual Conference of the International Speech Communication Association; 2012.
  • Chung J, Gulcehre C, Cho K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv Preprint ArXiv. 2014;1412:3555.
  • Cho K, Van Merrïenboer B, Bahdanau D, et al. On the properties of neural machine translation: encoder-decoder approaches. arXiv Preprint ArXiv. 2014;1409:1259.
  • Baziotis C, Pelekis N, Doulkeridis C. Deep LSTM with attention for message-level and topic-based sentiment analysis. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017); 2017. p. 747–754.
  • Wei H, Li M. Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code, In IJCAI; 2017. p. 3034–3040.
  • Wen T, Gasic M, Mrkˇsíc N, et al. Semantically conditioned LSTM-based natural language generation for spoken dialogue systems. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing; 2015. p. 1711–1721.
  • Hamdy A, Tazy M. Deep hybrid features or code smells detection. J Theor Appl Inf Technol. 2020;98:14.
  • Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015.
  • Kim Y. Convolutional Neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Qatar, Doha, Oct. 2014.
  • Johnson R, Zhang T. Semi-supervised convolutional neural networks for text categorization via region embedding. Adv Neural Inf Process Syst. 2015;1:919–927.
  • Turian J, Ratinov L, Bengio Y. Word representations: a simple and general method for semi-supervised learning. In Proceedings of the 48th annual meeting of the association for computational linguistics; 2010. p. 384–394.
  • Pagliardini M, Gupta P, Jaggi M. Unsupervised learning of sentence embeddings using compositional n-gram features. arXiv. 2017;1703:02507.
  • Palangi H, Deng L, Shen Y, et al. Deep sentence embedding using the long short term memory network: Analysis and application to information retrieval, CoRR, abs/1502.06922, 2015.
  • Le Q, Mikolov T. Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on Machine Learning, Beijing, China; 2014.
  • Goodfellow I, Bengio Y, Courville A, et al. Deep learning, Vol. 1. Cambridge: MIT press; 2016.
  • Natural Language Toolkit. https://www.nltk.org.
  • GENSIM Topic Modelling for Humans. https://pypi.org/project/gensim/.
  • Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on International Conference on Machine Learning-Volume 37. JMLR; 2015. p.448–456.
  • Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929–1958.
  • Keras Deepl Learning Framework. https://keras.io/.
  • Scikit Machine Learning for Python. https://scikit-learn.org.
  • Hamdy A, El-Laithy A. Multi-Feature approach for Bug severity assignment, International journal of open source software and Processes (IJOSSP). IGI Global. April 2020;11(2):1–15.

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