publication . Preprint . 2020

A Survey on Deep Learning for Software Engineering

Yang, Yanming; Xia, Xin; Lo, David; Grundy, John;
Open Access English
  • Published: 30 Nov 2020
Abstract
In 2006, Geoffrey Hinton proposed the concept of training ''Deep Neural Networks (DNNs)'' and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly used to develop state-of-the-art software engineering (SE) research tools due to its ability to boost performance for various SE tasks. There are many factors, e.g., deep learning model selection, internal structure differences, and model optimization techniques, that may have an impact on the performance of DN...
Subjects
free text keywords: Computer Science - Software Engineering
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148 references, page 1 of 10

[1] Aysh Al-Hroob, Ayad Tareq Imam, and Rawan Al-Heisa. 2018. The use of artificial neural networks for extracting actions and actors from requirements document. IST 101 (2018), 1-15.

[2] Mohammad Alahmadi, Abdulkarim Khormi, Biswas Parajuli, Jonathan Hassel, Sonia Haiduc, and Piyush Kumar. 2020. Code Localization in Programming Screencasts. ESE 25, 2 (2020), 1536-1572.

[3] Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Minghui Wu, and Xiaohu Yang. 2020. psc2code: Denoising Code Extraction from Programming Screencasts. TOSEM 29, 3 (2020), 1-38.

[4] Antoine Barbez, Foutse Khomh, and Yann-Gaël Guéhéneuc. 2019. Deep Learning Anti-patterns from Code Metrics History. In ICSME. IEEE, 114-124. [OpenAIRE]

[5] Raja Ben Abdessalem, Shiva Nejati, Lionel C Briand, and Thomas Stifter. 2016. Testing advanced driver assistance systems using multi-objective search and neural networks. In ASE. 63-74.

[6] Sahil Bhatia, Pushmeet Kohli, and Rishabh Singh. 2018. Neuro-symbolic program corrector for introductory programming assignments. In ICSE. IEEE, 60-70.

[7] Manjubala Bisi and Neeraj Kumar Goyal. 2016. Software development efforts prediction using artificial neural network. IETS 10, 3 (2016), 63-71.

[8] Pierre Bourque, Richard E Fairley, et al. 2014. Guide to the software engineering body of knowledge (SWEBOK (R)): Version 3.0. IEEE Computer Society Press.

[9] Lutz Büch and Artur Andrzejak. 2019. Learning-based recursive aggregation of abstract syntax trees for code clone detection. In SANER. IEEE, 95-104.

[10] Jose Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, and Satish Chandra. 2019. When deep learning met code search. In FSE. 964-974.

[11] Chao Chen, Wenrui Diao, Yingpei Zeng, Shanqing Guo, and Chengyu Hu. 2018. DRLgencert: Deep learning-based automated testing of certificate verification in SSL/TLS implementations. In ICSME. IEEE, 48-58.

[12] Chunyang Chen, Ting Su, Guozhu Meng, Zhenchang Xing, and Yang Liu. 2018. From ui design image to gui skeleton: a neural machine translator to bootstrap mobile gui implementation. In ICSE. 665-676.

[13] Guibin Chen, Chunyang Chen, Zhenchang Xing, and Bowen Xu. 2016. Learning a dual-language vector space for domain-specific cross-lingual question retrieval. In ASE. IEEE, 744-755.

[14] Jieshan Chen, Chunyang Chen, Zhenchang Xing, Xin Xia, Liming Zhu, John Grundy, and Jinshui Wang. 2020. Wireframe-based UI design search through image autoencoder. TOSEM 29, 3 (2020), 1-31.

[15] Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Aditya Ghose, and Tim Menzies. 2018. A deep learning model for estimating story points. TSE 45, 7 (2018), 637-656.

148 references, page 1 of 10
Abstract
In 2006, Geoffrey Hinton proposed the concept of training ''Deep Neural Networks (DNNs)'' and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly used to develop state-of-the-art software engineering (SE) research tools due to its ability to boost performance for various SE tasks. There are many factors, e.g., deep learning model selection, internal structure differences, and model optimization techniques, that may have an impact on the performance of DN...
Subjects
free text keywords: Computer Science - Software Engineering
Download from
148 references, page 1 of 10

[1] Aysh Al-Hroob, Ayad Tareq Imam, and Rawan Al-Heisa. 2018. The use of artificial neural networks for extracting actions and actors from requirements document. IST 101 (2018), 1-15.

[2] Mohammad Alahmadi, Abdulkarim Khormi, Biswas Parajuli, Jonathan Hassel, Sonia Haiduc, and Piyush Kumar. 2020. Code Localization in Programming Screencasts. ESE 25, 2 (2020), 1536-1572.

[3] Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Minghui Wu, and Xiaohu Yang. 2020. psc2code: Denoising Code Extraction from Programming Screencasts. TOSEM 29, 3 (2020), 1-38.

[4] Antoine Barbez, Foutse Khomh, and Yann-Gaël Guéhéneuc. 2019. Deep Learning Anti-patterns from Code Metrics History. In ICSME. IEEE, 114-124. [OpenAIRE]

[5] Raja Ben Abdessalem, Shiva Nejati, Lionel C Briand, and Thomas Stifter. 2016. Testing advanced driver assistance systems using multi-objective search and neural networks. In ASE. 63-74.

[6] Sahil Bhatia, Pushmeet Kohli, and Rishabh Singh. 2018. Neuro-symbolic program corrector for introductory programming assignments. In ICSE. IEEE, 60-70.

[7] Manjubala Bisi and Neeraj Kumar Goyal. 2016. Software development efforts prediction using artificial neural network. IETS 10, 3 (2016), 63-71.

[8] Pierre Bourque, Richard E Fairley, et al. 2014. Guide to the software engineering body of knowledge (SWEBOK (R)): Version 3.0. IEEE Computer Society Press.

[9] Lutz Büch and Artur Andrzejak. 2019. Learning-based recursive aggregation of abstract syntax trees for code clone detection. In SANER. IEEE, 95-104.

[10] Jose Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, and Satish Chandra. 2019. When deep learning met code search. In FSE. 964-974.

[11] Chao Chen, Wenrui Diao, Yingpei Zeng, Shanqing Guo, and Chengyu Hu. 2018. DRLgencert: Deep learning-based automated testing of certificate verification in SSL/TLS implementations. In ICSME. IEEE, 48-58.

[12] Chunyang Chen, Ting Su, Guozhu Meng, Zhenchang Xing, and Yang Liu. 2018. From ui design image to gui skeleton: a neural machine translator to bootstrap mobile gui implementation. In ICSE. 665-676.

[13] Guibin Chen, Chunyang Chen, Zhenchang Xing, and Bowen Xu. 2016. Learning a dual-language vector space for domain-specific cross-lingual question retrieval. In ASE. IEEE, 744-755.

[14] Jieshan Chen, Chunyang Chen, Zhenchang Xing, Xin Xia, Liming Zhu, John Grundy, and Jinshui Wang. 2020. Wireframe-based UI design search through image autoencoder. TOSEM 29, 3 (2020), 1-31.

[15] Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Aditya Ghose, and Tim Menzies. 2018. A deep learning model for estimating story points. TSE 45, 7 (2018), 637-656.

148 references, page 1 of 10
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