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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.
A logical calculus of the ideas immanent in nervous activity
Warren S McCulloch and Walter Pitts. 1943 · 1943
Earlier work this paper cites.
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.
The problem of overfitting
Douglas M Hawkins. 2004 · 2004
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. 2006 · 2006
Earlier work this paper cites.
Guidelines for performing systematic literature reviews in software engineering
Staffs Keele et al · 2007
Earlier work this paper cites.
Deep belief networks for phone recognition. In Nips workshop on deep learning for speech recognition and related applications , Vol. 1. Vancouver, Canada, 39
Abdel-rahman Mohamed, George Dahl, and Geoffrey Hinton. 2009 · 2009
Earlier work this paper cites.
Software requirements
Karl Wiegers and Joy Beatty. 2013 · 2013
Earlier work this paper cites.
Guide to the software engineering body of knowledge (SWEBOK (R)): Version 3.0
Pierre Bourque, Richard E Fairley, et al · 2014
Earlier work this paper cites.
A tutorial survey of architectures, algorithms, and applications for deep learning
Li Deng. 2014 · 2014
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015 · 2015
Earlier work this paper cites.
Neural networks for predicting the duration of new software projects
Cuauhtémoc López-Martín and Alain Abran. 2015 · 2015
Earlier work this paper cites.
Guidelines for conducting systematic mapping studies in software engineering: An update
Kai Petersen, Sairam Vakkalanka, and Ludwik Kuzniarz. 2015 · 2015
Earlier work this paper cites.
Toward deep learning software repositories. In MSR . IEEE, 334–345
Martin White, Christopher Vendome, Mario Linares-Vásquez, and Denys Poshyvanyk. 2015 · 2015
Earlier work this paper cites.
Testing advanced driver assistance systems using multi-objective search and neural networks. In ASE . 63–74
Raja Ben Abdessalem, Shiva Nejati, Lionel C Briand, and Thomas Stifter. 2016 · 2016
Earlier work this paper cites.
Software development efforts prediction using artificial neural network
Manjubala Bisi and Neeraj Kumar Goyal. 2016 · 2016
Earlier work this paper cites.
Learning a dual-language vector space for domain-specific cross-lingual question retrieval. In ASE . IEEE, 744–755
Guibin Chen, Chunyang Chen, Zhenchang Xing, and Bowen Xu. 2016 · 2016
Earlier work this paper cites.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016 · 2016
Earlier work this paper cites.
Deep API learning. In FSE . 631–642
Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, and Sunghun Kim. 2016 · 2016
Earlier work this paper cites.
Hybrid functional link artificial neural network approach for predicting maintainability of object-oriented software
Lov Kumar and Santanu Ku Rath. 2016 · 2016
Earlier work this paper cites.
Automatically learning semantic features for defect prediction. In ICSE . IEEE, 297–308
Song Wang, Taiyue Liu, and Lin Tan. 2016 · 2016
Earlier work this paper cites.
Deep learning code fragments for code clone detection. In ASE . IEEE, 87–98
Martin White, Michele Tufano, Christopher Vendome, and Denys Poshyvanyk. 2016 · 2016
Earlier work this paper cites.
Predicting semantically linkable knowledge in developer online forums via convolutional neural network. In ASE . IEEE, 51–62
Bowen Xu, Deheng Ye, Zhenchang Xing, Xin Xia, Guibin Chen, and Shanping Li. 2016 · 2016
Earlier work this paper cites.
Towards accurate duplicate bug retrieval using deep learning techniques. In ICSME . IEEE, 115–124
Jayati Deshmukh, KM Annervaz, Sanjay Podder, Shubhashis Sengupta, and Neville Dubash. 2017 · 2017
Earlier work this paper cites.
A systematic literature review: Opinion mining studies from mobile app store user reviews
Necmiye Genc-Nayebi and Alain Abran. 2017 · 2017
Earlier work this paper cites.
Learn&fuzz: Machine learning for input fuzzing. In ASE . IEEE, 50–59
Patrice Godefroid, Hila Peleg, and Rishabh Singh. 2017 · 2017
Earlier work this paper cites.
Learning to predict severity of software vulnerability using only vulnerability description. In ICSME . IEEE, 125–136
Zhuobing Han, Xiaohong Li, Zhenchang Xing, Hongtao Liu, and Zhiyong Feng. 2017 · 2017
Earlier work this paper cites.
A systematic literature review and meta-analysis on cross project defect prediction
Seyedrebvar Hosseini, Burak Turhan, and Dimuthu Gunarathna. 2017 · 2017
Earlier work this paper cites.
Automatically generating commit messages from diffs using neural machine translation. In ASE . IEEE, 135–146
Siyuan Jiang, Ameer Armaly, and Collin McMillan. 2017 · 2017
Earlier work this paper cites.
Bug localization with combination of deep learning and information retrieval. In ICPC . IEEE, 218–229
An Ngoc Lam, Anh Tuan Nguyen, Hoan Anh Nguyen, and Tien N Nguyen. 2017 · 2017
Earlier work this paper cites.
Cclearner: A deep learning-based clone detection approach. In ICSME . IEEE, 249–260
Liuqing Li, He Feng, Wenjie Zhuang, Na Meng, and Barbara Ryder. 2017 · 2017
Earlier work this paper cites.
Automatic text input generation for mobile testing. In ICSE . IEEE, 643–653
Peng Liu, Xiangyu Zhang, Marco Pistoia, Yunhui Zheng, Manoel Marques, and Lingfei Zeng. 2017 · 2017
Earlier work this paper cites.
Deep green: Modelling time-series of software energy consumption. In ICSME . IEEE, 273–283
Stephen Romansky, Neil C Borle, Shaiful Chowdhury, Abram Hindle, and Russ Greiner. 2017 · 2017
Earlier work this paper cites.
The use of artificial neural networks for extracting actions and actors from requirements document
Aysh Al-Hroob, Ayad Tareq Imam, and Rawan Al-Heisa. 2018 · 2018
Earlier work this paper cites.
Neuro-symbolic program corrector for introductory programming assignments. In ICSE . IEEE, 60–70
Sahil Bhatia, Pushmeet Kohli, and Rishabh Singh. 2018 · 2018
Earlier work this paper cites.
A deep learning model for estimating story points
Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Aditya Ghose, and Tim Menzies. 2018 · 2018
Earlier work this paper cites.
Compiler fuzzing through deep learning. In ISSTA . 95–105
Chris Cummins, Pavlos Petoumenos, Alastair Murray, and Hugh Leather. 2018 · 2018
Earlier work this paper cites.
Automatic feature learning for predicting vulnerable software components
Hoa Khanh Dam, Truyen Tran, Trang Thi Minh Pham, Shien Wee Ng, John Grundy, and Aditya Ghose. 2018 · 2018
Earlier work this paper cites.
Keep it simple: Is deep learning good for linguistic smell detection?. In SANER . IEEE, 602–611
Sarah Fakhoury, Venera Arnaoudova, Cedric Noiseux, Foutse Khomh, and Giuliano Antoniol. 2018 · 2018
Earlier work this paper cites.
Deep code search. In ICSE . IEEE, 933–944
Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. 2018 · 2018
Earlier work this paper cites.
Deep learning type inference. In FSE . 152–162
Vincent J Hellendoorn, Christian Bird, Earl T Barr, and Miltiadis Allamanis. 2018 · 2018
Earlier work this paper cites.
Deep code comment generation. In ICPC . IEEE, 200–20010
Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin. 2018 · 2018
Earlier work this paper cites.
Automating intention mining
Qiao Huang, Xin Xia, David Lo, and Gail C Murphy. 2018 · 2018
Earlier work this paper cites.
Using recurrent neural networks for decompilation. In SANER . IEEE, 346–356
Deborah S Katz, Jason Ruchti, and Eric Schulte. 2018 · 2018
Cited alongside, same era.
Deep specification mining. In Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis . 106–117
Tien-Duy B Le and David Lo. 2018 · 2018
Cited alongside, same era.
Adapting neural text classification for improved software categorization. In ICSME . IEEE, 461–472
Alexander LeClair, Zachary Eberhart, and Collin McMillan. 2018 · 2018
Cited alongside, same era.
FastTagRec: fast tag recommendation for software information sites
Jin Liu, Pingyi Zhou, Zijiang Yang, Xiao Liu, and John Grundy. 2018c · 2018
Cited alongside, same era.
500+ times faster than deep learning:(a case study exploring faster methods for text mining stackoverflow). In MSR . IEEE, 554–563
Tim Menzies, Suvodeep Majumder, Nikhila Balaji, Katie Brey, and Wei Fu. 2018 · 2018
Cited alongside, same era.
Cross-language clone detection by learning over abstract syntax trees. In MSR . IEEE, 518–528
Daniel Perez and Shigeru Chiba. 2019 · 2019
Later among the works it cites.
Extraction of system states from natural language requirements. In RE . IEEE, 211–222
Florian Pudlitz, Florian Brokhausen, and Andreas Vogelsang. 2019 · 2019
Later among the works it cites.
Neural network-based detection of self-admitted technical debt: from performance to explainability
Xiaoxue Ren, Zhenchang Xing, Xin Xia, David Lo, Xinyu Wang, and John Grundy. 2019 · 2019
Later among the works it cites.
DeepLink: Recovering issue-commit links based on deep learning
Hang Ruan, Bihuan Chen, Xin Peng, and Wenyun Zhao. 2019 · 2019
Later among the works it cites.
Feature maps: A comprehensible software representation for design pattern detection. In SANER . IEEE, 207–217
Hannes Thaller, Lukas Linsbauer, and Alexander Egyed. 2019 · 2019
Later among the works it cites.
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Improving code readability classification using convolutional neural networks
Qing Mi, Jacky Keung, Yan Xiao, Solomon Mensah, and Yujin Gao. 2018 · 2018
Cited alongside, same era.
Machine learning-based prototyping of graphical user interfaces for mobile apps
Kevin Patrick Moran, Carlos Bernal-Cárdenas, Michael Curcio, Richard Bonett, and Denys Poshyvanyk. 2018 · 2018
Cited alongside, same era.
A deep neural network language model with contexts for source code. In SANER . IEEE, 323–334
Anh Tuan Nguyen, Trong Duc Nguyen, Hung Dang Phan, and Tien N Nguyen. 2018 · 2018
Cited alongside, same era.
A deep learning approach to identifying source code in images and video. In MSR . IEEE, 376–386
Jordan Ott, Abigail Atchison, Paul Harnack, Adrienne Bergh, and Erik Linstead. 2018 · 2018
Cited alongside, same era.
Neural network for software reliability analysis of dynamically weighted NHPP growth models with imperfect debugging
Pooja Rani and GS Mahapatra. 2018 · 2018
Cited alongside, same era.
Syntax and sensibility: Using language models to detect and correct syntax errors. In SANER . IEEE, 311–322
Eddie Antonio Santos, Joshua Charles Campbell, Dhvani Patel, Abram Hindle, and José Nelson Amaral. 2018 · 2018
Cited alongside, same era.
Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning
Haonan Tong, Bin Liu, and Shihai Wang. 2018 · 2018
Cited alongside, same era.
An empirical study on learning bug-fixing patches in the wild via neural machine translation
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. 2019 · 2019
Later among the works it cites.
Domain-specific machine translation with recurrent neural network for software localization
Xu Wang, Chunyang Chen, and Zhenchang Xing. 2019a · 2019
Later among the works it cites.
Sorting and transforming program repair ingredients via deep learning code similarities. In SANER . IEEE, 479–490
Martin White, Michele Tufano, Matias Martinez, Martin Monperrus, and Denys Poshyvanyk. 2019 · 2019
Later among the works it cites.
Predicting How to Test Requirements: An Automated Approach. In RE . IEEE, 120–130
Jonas Paul Winkler, Jannis Grönberg, and Andreas Vogelsang. 2019 · 2019
Later among the works it cites.
Improving bug localization with word embedding and enhanced convolutional neural networks
Yan Xiao, Jacky Keung, Kwabena E Bennin, and Qing Mi. 2019 · 2019
Later among the works it cites.
DeepLink: A code knowledge graph based deep learning approach for issue-commit link recovery. In SANER . IEEE, 434–444
Rui Xie, Long Chen, Wei Ye, Zhiyu Li, Tianxiang Hu, Dongdong Du, and Shikun Zhang. 2019 · 2019
Later among the works it cites.
LDFR: Learning deep feature representation for software defect prediction
Zhou Xu, Shuai Li, Jun Xu, Jin Liu, Xiapu Luo, Yifeng Zhang, Tao Zhang, Jacky Keung, and Yutian Tang. 2019 · 2019
Later among the works it cites.
Neural detection of semantic code clones via tree-based convolution. In ICPC . IEEE, 70–80
Hao Yu, Wing Lam, Long Chen, Ge Li, Tao Xie, and Qianxiang Wang. 2019 · 2019
Later among the works it cites.
Wuji: Automatic online combat game testing using evolutionary deep reinforcement learning. In ASE . IEEE, 772–784
Yan Zheng, Xiaofei Xie, Ting Su, Lei Ma, Jianye Hao, Zhaopeng Meng, Yang Liu, Ruimin Shen, Yingfeng Chen, and Changjie Fan. 2019 · 2019
Later among the works it cites.
Is deep learning better than traditional approaches in tag recommendation for software information sites?
Pingyi Zhou, Jin Liu, Xiao Liu, Zijiang Yang, and John Grundy. 2019a · 2019
Later among the works it cites.
Improving defect prediction with deep forest
Tianchi Zhou, Xiaobing Sun, Xin Xia, Bin Li, and Xiang Chen. 2019b · 2019
Later among the works it cites.
Augmenting Java method comments generation with context information based on neural networks
Yu Zhou, Xin Yan, Wenhua Yang, Taolue Chen, and Zhiqiu Huang. 2019c · 2019
Later among the works it cites.
Code Localization in Programming Screencasts
Mohammad Alahmadi, Abdulkarim Khormi, Biswas Parajuli, Jonathan Hassel, Sonia Haiduc, and Piyush Kumar. 2020 · 2020
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psc2code: Denoising Code Extraction from Programming Screencasts
Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Minghui Wu, and Xiaohu Yang. 2020 · 2020
Closest in time.
Wireframe-based UI design search through image autoencoder
Jieshan Chen, Chunyang Chen, Zhenchang Xing, Xin Xia, Liming Zhu, John Grundy, and Jinshui Wang. 2020 · 2020
Closest in time.
Suggesting Comment Completions for Python using Neural Language Models. In SANER . IEEE, 456–467
Adelina Ciurumelea, Sebastian Proksch, and Harald C Gall. 2020 · 2020
Closest in time.
Virtualization of stateful services via machine learning
Hasan Ferit Enişer and Alper Sen. 2020 · 2020
Closest in time.
Functional code clone detection with syntax and semantics fusion learning. In ISSTA . 516–527
Chunrong Fang, Zixi Liu, Yangyang Shi, Jeff Huang, and Qingkai Shi. 2020 · 2020
Closest in time.
ThermoSim: Deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments
Sukhpal Singh Gill, Shreshth Tuli, Adel Nadjaran Toosi, Felix Cuadrado, Peter Garraghan, Rami Bahsoon, Hanan Lutfiyya, Rizos Sakellariou, Omer Rana, Schahram Dustdar, et al · 2020
Closest in time.
A Code-Description Representation Learning Model Based on Attention. In SANER . IEEE, 447–455
Qing Huang, An Qiu, Maosheng Zhong, and Yuan Wang. 2020 · 2020
Closest in time.
CodeGRU: Context-aware deep learning with gated recurrent unit for source code modeling
Yasir Hussain, Zhiqiu Huang, Yu Zhou, and Senzhang Wang. 2020 · 2020
Closest in time.
Predicting Node Failures in an Ultra-large-scale Cloud Computing Platform: an AIOps Solution
Yangguang Li, Zhen Ming Jiang, Heng Li, Ahmed E Hassan, Cheng He, Ruirui Huang, Zhengda Zeng, Mian Wang, and Pinan Chen. 2020a · 2020
Closest in time.
On the Replicability and Reproducibility of Deep Learning in Software Engineering
Chao Liu, Cuiyun Gao, Xin Xia, David Lo, John Grundy, and Xiaohu Yang. 2020a · 2020
Closest in time.
DeepSQLi: Deep Semantic Learning for Testing SQL Injection
Muyang Liu, Ke Li, and Tao Chen. 2020b · 2020
Closest in time.
CoCoNuT: combining context-aware neural translation models using ensemble for program repair. In ISSTA . 101–114
Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, and Lin Tan. 2020 · 2020
Closest in time.
Cross-Dataset Design Discussion Mining. In SANER . IEEE, 149–160
Alvi Mahadi, Karan Tongay, and Neil A Ernst. 2020 · 2020
Closest in time.
Effective modeling of encoder-decoder architecture for joint entity and relation extraction. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 8528–8535
Tapas Nayak and Hwee Tou Ng. 2020 · 2020
Closest in time.
Analyzing bug fix for automatic bug cause classification
Zhen Ni, Bin Li, Xiaobing Sun, Tianhao Chen, Ben Tang, and Xinchen Shi. 2020 · 2020
Closest in time.
Deep learning model for end-to-end approximation of COSMIC functional size based on use-case names
Mirosław Ochodek, Sylwia Kopczyńska, and Miroslaw Staron. 2020 · 2020
Closest in time.
Reinforcement learning based curiosity-driven testing of Android applications. In ISSTA . 153–164
Minxue Pan, An Huang, Guoxin Wang, Tian Zhang, and Xuandong Li. 2020 · 2020
Closest in time.
On an optimal analogy-based software effort estimation
Passakorn Phannachitta. 2020 · 2020
Closest in time.
CORE: Automating Review Recommendation for Code Changes. In SANER . IEEE, 284–295
Jing Kai Siow, Cuiyun Gao, Lingling Fan, Sen Chen, and Yang Liu. 2020 · 2020
Closest in time.
Software trustworthiness evaluation model based on a behaviour trajectory matrix
Junfeng Tian and Yuhui Guo. 2020 · 2020
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BVDetector: A program slice-based binary code vulnerability intelligent detection system
Junfeng Tian, Wenjing Xing, and Zhen Li. 2020 · 2020
Closest in time.
Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree. In SANER . IEEE, 261–271
Wenhan Wang, Ge Li, Bo Ma, Xin Xia, and Zhi Jin. 2020 · 2020
Closest in time.
Automatically learning patterns for self-admitted technical debt removal. In SANER . IEEE, 355–366
Fiorella Zampetti, Alexander Serebrenik, and Massimiliano Di Penta. 2020 · 2020
Closest in time.
Improving software bug-specific named entity recognition with deep neural network
Cheng Zhou, Bin Li, and Xiaobing Sun. 2020 · 2020
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