Defects are common in software systems and can potentially cause various problems to software users.
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Advances in Neural Information Processing Systems 2
Y. Le Cun, B. Boser, J. S. Denker, R. E. Howard, W. Habbard, L. D. Jackel, and D. Henderson. 1990 · 1990
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Random forests
L. Breiman. 2001 · 2001
Earlier work this paper cites.
Use of Relative Code Churn Measures to Predict System Defect Density. In Proceedings of the 27th International Conference on Software Engineering
Nachiappan Nagappan and Thomas Ball. 2005 · 2005
Earlier work this paper cites.
Reducing the Dimensionality of Data with Neural Networks
Geoffrey Hinton and Ruslan Salakhutdinov. 2006 · 2006
Earlier work this paper cites.
A Systematic Review of Software Fault Prediction Studies
Cagatay Catal and Banu Diri. 2009 · 2008
Earlier work this paper cites.
A Comparative Analysis of the Efficiency of Change Metrics and Static Code Attributes for Defect Prediction. In Proceedings of the 30th International Conference on Software Engineering
Raimund Moser, Witold Pedrycz, and Giancarlo Succi. 2008 · 2008
Earlier work this paper cites.
The Influence of Organizational Structure on Software Quality: An Empirical Case Study. In Proceedings of the 30th International Conference on Software Engineering
Nachiappan Nagappan, Brendan Murphy, and Victor Basili. 2008 · 2008
Earlier work this paper cites.
Can Developer-module Networks Predict Failures?. In Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering
Martin Pinzger, Nachiappan Nagappan, and Brendan Murphy. 2008 · 2008
Earlier work this paper cites.
Predicting defects using network analysis on dependency graphs. In 2008 ACM/IEEE 30th International Conference on Software Engineering
T. Zimmermann and N. Nagappan. 2008 · 2008
Cited alongside, same era.
Predicting Faults Using the Complexity of Code Changes. In Proceedings of the 31st International Conference on Software Engineering
Ahmed E. Hassan. 2009 · 2009
Cited alongside, same era.
A Systematic Literature Review on Fault Prediction Performance in Software Engineering
Tracy Hall, Sarah Beecham, David Bowes, David Gray, and Steve Counsell. 2012 · 2011
Cited alongside, same era.
Evaluating Defect Prediction Approaches: A Benchmark and an Extensive Comparison
Marco D’Ambros, Michele Lanza, and Romain Robbes. 2012 · 2012
Cited alongside, same era.
Method-level Bug Prediction. In Proceedings of the ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Emanuel Giger, Marco D’Ambros, Martin Pinzger, and Harald C. Gall. 2012 · 2012
Cited alongside, same era.
Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael U Gutmann and Aapo Hyvärinen. 2012 · 2012
Cited alongside, same era.
A Hitchhiker’s guide to statistical tests for assessing randomized algorithms in software engineering
Andrea Arcuri and Lionel Briand. 2014 · 2014
Cited alongside, same era.
Towards Building a Universal Defect Prediction Model. In Proceedings of the 11th Working Conference on Mining Software Repositories
Feng Zhang, Audris Mockus, Iman Keivanloo, and Ying Zou. 2014 · 2014
Cited alongside, same era.
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, July 26-31, 2015, Beijing, China, Volume 1: Long Papers
Kai Sheng Tai, Richard Socher, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Toward Deep Learning Software Repositories. In Proceedings of the 12th Working Conference on Mining Software Repositories
Martin White, Christopher Vendome, Mario Linares-Vásquez, and Denys Poshyvanyk. 2015 · 2015
Cited alongside, same era.
DeepSoft: A vision for a deep model of software. In Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering
Hoa Khanh Dam, Truyen Tran, John Grundy, and Aditya Ghose. 2016b
Cited in the paper.
A deep language model for software code. In Workshop on Naturalness of Software (NL+SE), co-located with the 24th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE)
Hoa Khanh Dam, Truyen Tran, and Trang Pham. 2016a
Cited in the paper.
Deep Learning for Just-in-Time Defect Prediction. In Proceedings of the 2015 IEEE International Conference on Software Quality, Reliability and Security
Xinli Yang, David Lo, Xin Xia, Yun Zhang, and Jianling Sun. 2015 · 2015
Later among the works it cites.
Deep API Learning. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering
Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, and Sunghun Kim. 2016 · 2016
Later among the works it cites.
Learning Unified Features from Natural and Programming Languages for Locating Buggy Source Code. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Xuan Huo, Ming Li, and Zhi-Hua Zhou. 2016 · 2016
Later among the works it cites.
Defect Prediction: Accomplishments and Future Challenges. In Leaders of Tomorrow Symposium: Future of Software Engineering, FOSE@SANER 2016, Osaka, Japan, March 14, 2016
Yasutaka Kamei and Emad Shihab. 2016 · 2016
Later among the works it cites.
On the “Naturalness” of Buggy Code. In Proceedings of the 38th International Conference on Software Engineering
Baishakhi Ray, Vincent Hellendoorn, Saheel Godhane, Zhaopeng Tu, Alberto Bacchelli, and Premkumar Devanbu. 2016 · 2016
Later among the works it cites.
Automatically Learning Semantic Features for Defect Prediction. In Proceedings of the 38th International Conference on Software Engineering
Song Wang, Taiyue Liu, and Lin Tan. 2016 · 2016
Later among the works it cites.
Deep Learning Code Fragments for Code Clone Detection. In Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering
Martin White, Michele Tufano, Christopher Vendome, and Denys Poshyvanyk. 2016 · 2016
Later among the works it cites.
DeepFix: Fixing Common C Language Errors by Deep Learning. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA
Rahul Gupta, Soham Pal, Aditya Kanade, and Shirish Shevade. 2017 · 2017
Later among the works it cites.
Keras: Deep Learning library for Theano and TensorFlow
Keras · 2017
Later among the works it cites.
Software Defect Prediction via Convolutional Neural Network. In 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)
J. Li, P. He, J. Zhu, and M. R. Lyu. 2017 · 2017
Later among the works it cites.
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