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Deep Learning (DL) is being used nowadays in many traditional Software Engineering (SE) problems and tasks.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,”
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,”
2015
Earlier work this paper cites.
M. White, C. Vendome, M. Linares-Vásquez, and D. Poshyvanyk, “Toward deep learning software repositories,” in
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
S. Wang, T. Liu, and L. Tan, “Automatically learning semantic features for defect prediction,” in
2016
Earlier work this paper cites.
I. Goodfellow, Y. Bengio, and A. Courville,
2016
Earlier work this paper cites.
B. Xu, D. Ye, Z. Xing, X. Xia, G. Chen, and S. Li, “Predicting semantically linkable knowledge in developer online forums via convolutional neural network,” in
2016
Earlier work this paper cites.
G. Chen, C. Chen, Z. Xing, and B. Xu, “Learning a dual-language vector space for domain-specific cross-lingual question retrieval,” in
2016
Earlier work this paper cites.
X. Gu, H. Zhang, D. Zhang, and S. Kim, “Deep API learning,” in
2016
Earlier work this paper cites.
M. White, M. Tufano, C. Vendome, and D. Poshyvanyk, “Deep learning code fragments for code clone detection,” in
2016
Earlier work this paper cites.
P. Louridas and C. Ebert, “Machine learning,”
2016
Earlier work this paper cites.
P. Liu, X. Zhang, M. Pistoia, Y. Zheng, M. Marques, and L. Zeng, “Automatic text input generation for mobile testing,” in
2017
Earlier work this paper cites.
Yu Wang, “A new concept using lstm neural networks for dynamic system identification,” in
2017
Earlier work this paper cites.
W. Fu and T. Menzies, “Easy over hard: A case study on deep learning,” in
2017
Earlier work this paper cites.
P. Godefroid, H. Peleg, and R. Singh, “Learn&Fuzz: Machine learning for input fuzzing,” in
2017
Earlier work this paper cites.
Z. Han, X. Li, Z. Xing, H. Liu, and Z. Feng, “Learning to predict severity of software vulnerability using only vulnerability description,” in
2017
Earlier work this paper cites.
V. J. Hellendoorn and P. Devanbu, “Are deep neural networks the best choice for modeling source code?” in
2017
Earlier work this paper cites.
A. N. Lam, A. T. Nguyen, H. A. Nguyen, and T. N. Nguyen, “Bug localization with combination of deep learning and information retrieval,” in
2017
Earlier work this paper cites.
L. Li, H. Feng, W. Zhuang, N. Meng, and B. Ryder, “CCLearner: A deep learning-based clone detection approach,” in
2017
Earlier work this paper cites.
J. Deshmukh, A. K. M, S. Podder, S. Sengupta, and N. Dubash, “Towards accurate duplicate bug retrieval using deep learning techniques,” in
2017
Earlier work this paper cites.
J. Guo, J. Cheng, and J. Cleland-Huang, “Semantically enhanced software traceability using deep learning techniques,” in
2017
Earlier work this paper cites.
F. Falcini, G. Lami, and A. M. Costanza, “Deep learning in automotive software,”
2017
Earlier work this paper cites.
F. Falcini, G. Lami, and A. Mitidieri, “Yet another challenge for the automotive software: Deep learning,”
2017
Earlier work this paper cites.
M. Mohammadi, A. Al-Fuqaha, M. Guizani, and J. Oh, “Semisupervised deep reinforcement learning in support of iot and smart city services,”
2018
Earlier work this paper cites.
S. Majumder, N. Balaji, K. Brey, W. Fu, and T. Menzies, “500+ times faster than deep learning: A case study exploring faster methods for text mining StackOverflow,” in
2018
Earlier work this paper cites.
J. Ott, A. Atchison, P. Harnack, A. Bergh, and E. Linstead, “A deep learning approach to identifying source code in images and video,” in
2018
Earlier work this paper cites.
H. Tong, B. Liu, and S. Wang, “Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning,”
2018
Earlier work this paper cites.
S. Wang, T. Liu, J. Nam, and L. Tan, “Deep semantic feature learning for software defect prediction,”
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
L. Ma, F. Juefei-Xu, F. Zhang, J. Sun, M. Xue, B. Li, C. Chen, T. Su, L. Li, Y. Liu, J. Zhao, and Y. Wang, “DeepGauge: Multi-granularity testing criteria for deep learning systems,” in
2018
Cited alongside, same era.
Y. Tian, K. Pei, S. Jana, and B. Ray, “DeepTest: Automated testing of deep-neural-network-driven autonomous cars,” in
2018
Cited alongside, same era.
A. Dwarakanath, M. Ahuja, S. Sikand, R. M. Rao, R. P. J. C. Bose, N. Dubash, and S. Podder, “Identifying implementation bugs in machine learning based image classifiers using metamorphic testing,” in
2018
Cited alongside, same era.
H. V. Pham, T. Lutellier, W. Qi, and L. Tan, “CRADLE: Cross-backend validation to detect and localize bugs in deep learning libraries,” in
2019
Closest in time.
X. Xie, L. Ma, F. Juefei-Xu, M. Xue, H. Chen, Y. Liu, J. Zhao, B. Li, J. Yin, and S. See, “DeepHunter: A coverage-guided fuzz testing framework for deep neural networks,” in
2019
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J. Kim, R. Feldt, and S. Yoo, “Guiding deep learning system testing using surprise adequacy,” in
2019
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X. Du, X. Xie, Y. Li, L. Ma, Y. Liu, and J. Zhao, “DeepStellar: Model-based quantitative analysis of stateful deep learning systems,” in
2019
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M. J. Islam, G. Nguyen, R. Pan, and H. Rajan, “A comprehensive study on deep learning bug characteristics,” in
2019
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2018
Cited alongside, same era.
Y. Zhang, Y. Chen, S.-C. Cheung, Y. Xiong, and L. Zhang, “An empirical study on TensorFlow program bugs,” in
2018
Cited alongside, same era.
B. Xu, A. Shirani, D. Lo, and M. A. Alipour, “Prediction of relatedness in Stack Overflow: Deep learning vs. svm: A reproducibility study,” in
2018
Cited alongside, same era.
X. Hu, G. Li, X. Xia, D. Lo, and Z. Jin, “Deep code comment generation,” in
2018
Cited alongside, same era.
C. Cummins, P. Petoumenos, A. Murray, and H. Leather, “Compiler fuzzing through deep learning,” in
2018
Cited alongside, same era.
M. Wen, R. Wu, and S. C. Cheung, “How well do change sequences predict defects? sequence learning from software changes,”
2018
Cited alongside, same era.
X. Gu, H. Zhang, and S. Kim, “Deep code search,” in
2018
Cited alongside, same era.
H. K. Dam, T. Tran, T. T. M. Pham, S. W. Ng, J. Grundy, and A. Ghose, “Automatic feature learning for predicting vulnerable software components,”
2018
Cited alongside, same era.
S. Wang, N. Phan, Y. Wang, and Y. Zhao, “Extracting API tips from developer question and answer websites,” in
2019
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D. Zhao, Z. Xing, C. Chen, X. Xia, and G. Li, “ActionNet: Vision-based workflow action recognition from programming screencasts,” in
2019
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H. Zhao, Z. Li, H. Wei, J. Shi, and Y. Huang, “SeqFuzzer: An industrial protocol fuzzing framework from a deep learning perspective,” in
2019
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Z. Zhang, Y. Lei, X. Mao, and P. Li, “CNN-FL: An effective approach for localizing faults using convolutional neural networks,” in
2019
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H. K. Dam, T. Pham, S. W. Ng, T. Tran, J. Grundy, A. Ghose, T. Kim, and C.-J. Kim, “Lessons learned from using a deep tree-based model for software defect prediction in practice,” in
2019
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T. Zhou, X. Sun, X. Xia, B. Li, and X. Chen, “Improving defect prediction with deep forest,”
2019
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Q. Huang, Y. Yang, and M. Cheng, “Deep learning the semantics of change sequences for query expansion,”
2019
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J. Cambronero, H. Li, S. Kim, K. Sen, and S. Chandra, “When deep learning met code search,” in
2019
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X. Huo, F. Thung, M. Li, D. Lo, and S. Shi, “Deep transfer bug localization,”
2019
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Y. Xiao, J. Keung, K. E. Bennin, and Q. Mi, “Improving bug localization with word embedding and enhanced convolutional neural networks,”
2019
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H. Yu, W. Lam, L. Chen, G. Li, T. Xie, and Q. Wang, “Neural detection of semantic code clones via tree-based convolution,” in
2019
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H. Liu, J. Jin, Z. Xu, Y. Bu, Y. Zou, and L. Zhang, “Deep learning based code smell detection,”
2019
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C. Guo, D. Huang, N. Dong, Q. Ye, J. Xu, Y. Fan, H. Yang, and Y. Xu, “Deep review sharing,” in
2019
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C. Guo, W. Wang, Y. Wu, N. Dong, Q. Ye, J. Xu, and S. Zhang, “Systematic comprehension for developer reply in mobile system forum,”
2019
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M. Tufano, J. Pantiuchina, C. Watson, G. Bavota, and D. Poshyvanyk, “On learning meaningful code changes via neural machine translation,” in
2019
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M. White, M. Tufano, M. Martinez, M. Monperrus, and D. Poshyvanyk, “Sorting and transforming program repair ingredients via deep learning code similarities,” in
2019
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H. Sankar, V. Subramaniyaswamy, V. Vijayakumar, S. A. Kumar, R. Logesh, and A. Umamakeswari, “Intelligent sentiment analysis approach using edge computing-based deep learning technique,”
2019
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R. S. Malik, J. Patra, and M. Pradel, “NL2Type: Inferring JavaScript function types from natural language information,” in
2019
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X. Zhang, Y. Xu, Q. Lin, B. Qiao, H. Zhang, Y. Dang, C. Xie, X. Yang, Q. Cheng, Z. Li, J. Chen, X. He, R. Yao, J.-G. Lou, M. Chintalapati, F. Shen, and D. Zhang, “Robust log-based anomaly detection on unstable log data,” in
2019
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C. Chen, Z. Xing, Y. Liu, and K. L. X. Ong, “Mining likely analogical apis across third-party libraries via large-scale unsupervised API semantics embedding,”
2019
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H. Thaller, L. Linsbauer, and A. Egyed, “Feature maps: A comprehensible software representation for design pattern detection,” in
2019
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M. Choetkiertikul, H. K. Dam, T. Tran, T. Pham, A. Ghose, and T. Menzies, “A deep learning model for estimating story points,”
2019
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C. Wang, X. Peng, M. Liu, Z. Xing, X. Bai, B. Xie, and T. Wang, “A learning-based approach for automatic construction of domain glossary from source code and documentation,” in
2019
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R. Xie, L. Chen, W. Ye, Z. Li, T. Hu, D. Du, and S. Zhang, “DeepLink: A code knowledge graph based deep learning approach for issue-commit link recovery,” in
2019
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