ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking
暂无分享,去创建一个
Yang Liu | Cuiyun Gao | Sen Chen | Shangqing Liu | L. Nie
[1] Koray Kavukcuoglu,et al. Visual Attention , 2020, Computational Models for Cognitive Vision.
[2] Lingling Fan,et al. CORE: Automating Review Recommendation for Code Changes , 2019, 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER).
[3] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[4] M. Post,et al. Generating Commit Messages from Git Diffs , 2019, ArXiv.
[5] Michele Bezzi,et al. Commit2Vec: Learning Distributed Representations of Code Changes , 2019, SN Computer Science.
[6] Shuyao Jiang,et al. Boosting Neural Commit Message Generation with Code Semantic Analysis , 2019, 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[7] He Jiang,et al. Machine Learning Based Recommendation of Method Names: How Far are We , 2019, 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[8] Michael R. Lyu,et al. Automating App Review Response Generation , 2019, 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[9] Shangqing Liu,et al. Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks , 2019, NeurIPS.
[10] Feng Xu,et al. Commit Message Generation for Source Code Changes , 2019, IJCAI.
[11] Yu Qian,et al. Generating Commit Messages from Diffs using Pointer-Generator Network , 2019, 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR).
[12] Xiaodong Liu,et al. A Hybrid Retrieval-Generation Neural Conversation Model , 2019, CIKM.
[13] Collin McMillan,et al. Recommendations for Datasets for Source Code Summarization , 2019, NAACL.
[14] Mikael Olsson. Structs , 2018, Modern C Quick Syntax Reference.
[15] Yutaka Matsuo,et al. Content Aware Source Code Change Description Generation , 2018, INLG.
[16] Marc Brockschmidt,et al. Structured Neural Summarization , 2018, ICLR.
[17] Zhenchang Xing,et al. Neural-Machine-Translation-Based Commit Message Generation: How Far Are We? , 2018, 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE).
[18] Philip S. Yu,et al. Improving Automatic Source Code Summarization via Deep Reinforcement Learning , 2018, 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE).
[19] Omer Levy,et al. code2seq: Generating Sequences from Structured Representations of Code , 2018, ICLR.
[20] Shuai Lu,et al. Summarizing Source Code with Transferred API Knowledge , 2018, IJCAI.
[21] Jian-Yun Nie,et al. An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems , 2018, IJCAI.
[22] David Lo,et al. Deep Code Comment Generation , 2018, 2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC).
[23] Omer Levy,et al. code2vec: learning distributed representations of code , 2018, Proc. ACM Program. Lang..
[24] Uri Alon,et al. A general path-based representation for predicting program properties , 2018, PLDI.
[25] Xiaonan Luo,et al. Mining Version Control System for Automatically Generating Commit Comment , 2017, 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM).
[26] Marc Brockschmidt,et al. Learning to Represent Programs with Graphs , 2017, ICLR.
[27] Collin McMillan,et al. Automatically generating commit messages from diffs using neural machine translation , 2017, 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE).
[28] Omer Levy,et al. Zero-Shot Relation Extraction via Reading Comprehension , 2017, CoNLL.
[29] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[30] Yutaka Matsuo,et al. A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes , 2017, ACL.
[31] Christopher D. Manning,et al. Get To The Point: Summarization with Pointer-Generator Networks , 2017, ACL.
[32] Collin McMillan,et al. Towards Automatic Generation of Short Summaries of Commits , 2017, 2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC).
[33] Rico Sennrich,et al. Nematus: a Toolkit for Neural Machine Translation , 2017, EACL.
[34] Paolo Frasconi,et al. Forward and Reverse Gradient-Based Hyperparameter Optimization , 2017, ICML.
[35] Quoc V. Le,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[36] Alvin Cheung,et al. Summarizing Source Code using a Neural Attention Model , 2016, ACL.
[37] Bin Li,et al. On Automatic Summarization of What and Why Information in Source Code Changes , 2016, 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC).
[38] Xiaodong Gu,et al. Deep API learning , 2016, SIGSOFT FSE.
[39] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[40] Xueqi Cheng,et al. Text Matching as Image Recognition , 2016, AAAI.
[41] Charles A. Sutton,et al. A Convolutional Attention Network for Extreme Summarization of Source Code , 2016, ICML.
[42] Jonathan I. Maletic,et al. Using stereotypes in the automatic generation of natural language summaries for C++ methods , 2015, 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME).
[43] Christopher D. Manning,et al. Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.
[44] Samy Bengio,et al. Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks , 2015, NIPS.
[45] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[46] Mario Linares Vásquez,et al. ChangeScribe: A Tool for Automatically Generating Commit Messages , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[47] Lin Tan,et al. CloCom: Mining existing source code for automatic comment generation , 2015, 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER).
[48] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[49] Yoshua Bengio,et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.
[50] Hang Li,et al. Convolutional Neural Network Architectures for Matching Natural Language Sentences , 2014, NIPS.
[51] Mario Linares Vásquez,et al. On Automatically Generating Commit Messages via Summarization of Source Code Changes , 2014, 2014 IEEE 14th International Working Conference on Source Code Analysis and Manipulation.
[52] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[53] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[54] Collin McMillan,et al. Improving topic model source code summarization , 2014, ICPC 2014.
[55] Collin McMillan,et al. Improving automated source code summarization via an eye-tracking study of programmers , 2014, ICSE.
[56] Lori L. Pollock,et al. Automatic generation of natural language summaries for Java classes , 2013, 2013 21st International Conference on Program Comprehension (ICPC).
[57] Hridesh Rajan,et al. Boa: A language and infrastructure for analyzing ultra-large-scale software repositories , 2013, 2013 35th International Conference on Software Engineering (ICSE).
[58] Kevin A Hallgren,et al. Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial. , 2012, Tutorials in quantitative methods for psychology.
[59] Andrian Marcus,et al. On the Use of Automated Text Summarization Techniques for Summarizing Source Code , 2010, 2010 17th Working Conference on Reverse Engineering.
[60] Emily Hill,et al. Towards automatically generating summary comments for Java methods , 2010, ASE.
[61] Westley Weimer,et al. Automatically documenting program changes , 2010, ASE.
[62] Andrian Marcus,et al. Supporting program comprehension with source code summarization , 2010, 2010 ACM/IEEE 32nd International Conference on Software Engineering.
[63] Documentation , 2006 .
[64] A. Lavie,et al. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments , 2005, IEEvaluation@ACL.
[65] Chin-Yew Lin,et al. ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.
[66] Salim Roukos,et al. Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.
[67] Jürgen Schmidhuber,et al. Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.
[68] S. Hochreiter,et al. Long Short-Term Memory , 1997, Neural Computation.
[69] Alexander J. Smola,et al. Support Vector Regression Machines , 1996, NIPS.
[70] Emanuele Della Valle,et al. An Introduction to Information Retrieval , 2013 .
[71] Akiko Aizawa,et al. An information-theoretic perspective of tf-idf measures , 2003, Inf. Process. Manag..
[72] R. Lewis. An Introduction to Classification and Regression Tree (CART) Analysis , 2000 .
[73] Jonathan Knudsen,et al. Learning Java , 2000 .
[74] C. Stein,et al. Estimation with Quadratic Loss , 1992 .
[75] Andy Davis,et al. This Paper Is Included in the Proceedings of the 12th Usenix Symposium on Operating Systems Design and Implementation (osdi '16). Tensorflow: a System for Large-scale Machine Learning Tensorflow: a System for Large-scale Machine Learning , 2022 .