Resources

This Github repository summarizes method name prediction paper resources. For more details, please refer to our paper Abstract Syntax Tree for Method Name Prediction: How Far Are We?

Table of Contents

• Papers list

• Experiments

Papers List

• A General Path-Based Representation for Predicting Program Properties.[pdf]
  • Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav. PLDI, 2018.
• Characterizing the Natural Language Descriptions in Software Logging Statements.[pdf][code]
  • Pinjia He, Zhuangbin Chen, Shilin He, Michael R. Lyu. ASE, 2018.
• code2vec: Learning Distributed Representations of Code.[pdf][code]
  • Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav. POPL, 2019.
• code2seq: Generating Sequences from Structured Representations of Code.[pdf][code]
  • Uri Alon, Shaked Brody, Omer Levy, Eran Yahav. ICLR, 2019.
• Machine Learning Based Recommendation of Method Names: How Far are We.[pdf][code]
  • Lin Jiang, Hui Liu, He Jiang. ASE, 2019.
• Mercem: Method Name Recommendation Based on Call Graph Embedding.[pdf][code]
  • Hiroshi Yonai, Yasuhiro Hayase, Hiroyuki Kitagawa. APSEC, 2019.
• Method Name Suggestion with Hierarchical Attention Networks.[pdf][code]
  • Sihan Xu, Sen Zhang, Weijing Wang, Xinya Cao, Chenkai Guo, Jing Xu. PEPM@POPL, 2019.
• Recovering Variable Names for Minified Code with Usage Contexts.[pdf][code]
  • Hieu Tran, Ngoc M. Tran, Son Nguyen, Hoan Nguyen, Tien N. Nguyen. ICSE, 2019.
• Embedding Java Classes with code2vec: Improvements from Variable Obfuscation.[pdf][code]
  • Rhys Compton, Eibe Frank, Panos Patros, Abigail Koay. MSR, 2020.
• Learning Semantic Program Embeddings with Graph Interval Neural Network.[pdf][code]
  • Yu Wang, Ke Wang, Fengjuan Gao, Linzhang Wang. OOPSLA, 2020.
• Towards Demystifying Dimensions of Source Code Embeddings.[pdf][code]
  • Md. Rafiqul Islam Rabin, Arjun Mukherjee, Omprakash Gnawali, Mohammad Amin Alipour. arXiv, 2020.
• Suggesting Natural Method Names to Check Name Consistencies.[pdf][code]
  • Son Nguyen, Hung Phan, Trinh Le, Tien N. Nguyen:. ICSE, 2020.
• Blended, Precise Semantic Program Embeddings.[pdf]
  • Ke Wang, Zhendong Su. PLDI, 2020.
• InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees.[pdf][code]
  • Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang. ICSE, 2021.
• A Mocktail of Source Code Representations.[pdf][code]
  • Dheeraj Vagavolu, Karthik Chandra Swarna, Sridhar Chimalakonda. ASE, 2021.
• A Lightweight Framework for Function Name Reassignment Based on Large-scale Stripped Binaries.[pdf][code]
  • Han Gao, Shaoyin Cheng, Yinxing Xue, Weiming Zhang. ISSTA, 2021.
• PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code.[pdf][code]
  • Egor Spirin, Egor Bogomolov, Vladimir Kovalenko, Timofey Bryksin. MSR, 2021.
• A Hybrid Code Representation Learning Approach for Predicting Method Names.[pdf][code]
  • Fengyi Zhang, Bihuan Chen, Rongfan Li, Xin Peng. Journal of Systems and Software, 2021.
• Universal Representation for Code.[pdf][code]
  • Linfeng Liu, Hoan Nguyen, George Karypis, Srinivasan Sengamedu. PAKDD, 2021.
• Lightweight Global and Local Contexts Guided Method Name Recommendation with Prior Knowledge.[pdf][code]
  • Shangwen Wang, Ming Wen, Bo Lin, Xiaoguang Mao. FSE, 2021.
• A Context-based Automated Approach for Method Name Consistency Checking and Suggestion.[pdf][code]
  • Yi Li, Shaohua Wang, Tien N. Nguyen. ICSE, 2021.
• Thinking Like a Developer? Comparing the Attention of Humans with Neural Models of Code.[pdf][code]
  • Matteo Paltenghi, Michael Pradel. ASE, 2021.
• Learning to Find Naming Issues with Big Code and Small Supervision.[pdf][code]
  • Jingxuan He, Cheng-Chun Lee, Veselin Raychev, Martin T. Vechev. PLDI, 2021.
• GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses.[pdf][code]
  • Wei Ma, Mengjie Zhao, Ezekiel O. Soremekun, Qiang Hu, Jie M. Zhang, Mike Papadakis, Maxime Cordy, Xiaofei Xie, Yves Le Traon. MSR, 2022.
• Learning to Represent Programs with Heterogeneous Graphs.[pdf][code]
  • Kechi Zhang, Wenhan Wang, Huangzhao Zhang, Ge Li, Zhi Jin. ICPC, 2022.
• Multilingual Training for Software Engineering.[pdf][code]
  • Toufique Ahmed, Premkumar T. Devanbu. ICSE, 2022.
• Learning to Recommend Method Names with Global Context.[pdf]
  • Fang Liu, Ge Li, Zhiyi Fu, Shuai Lu, Yiyang Hao, Zhi Jin. ICSE, 2022.
• SymLM: Predicting Function Names in Stripped Binaries via Context-Sensitive Execution-Aware Code Embeddings.[pdf][code]
  • Xin Jin, Kexin Pei, Jun Yeon Won, Zhiqiang Lin. CCS, 2022.
• Method Name Prediction for Automatically Generated Unit Tests.[pdf][code]
  • Maxim Petukhov, Evelina Gudauskayte, Arman Kaliyev, Mikhail Oskin, Dmitry Ivanov, Qianxiang Wang. ICCQ, 2022.
• A Naming Pattern Based Approach for Method Name Recommendation.[pdf][code]
  • Yanping Yang, Ling Xu, Meng Yan, Zhou Xu, Zhongyang Deng. ISSRE, 2022.
• A Review on Source Code Documentation.[pdf]
  • Sawan Rai, Ramesh Chandra Belwal, Atul Gupta. ACM Transactions on Intelligent Systems and Technology, 2022.
• Method name recommendation based on source code metrics.[pdf][code]
  • Saeed Parsa, Morteza Zakeri Nasrabadi, Masoud Ekhtiarzadeh, Mohammad Ramezani. Journal of Computer Languages, 2023.
• Fold2Vec: Towards a Statement-Based Representation of Code for Code Comprehension.[pdf][code]
  • Francesco Bertolotti, Walter Cazzola. ACM Transactions on Software Engineering and Methodology, 2023.

Experiments

You can use the code from the folder code and our experiments are based on them.