CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation

Control flow analysis

Frances E Allen. 1970 · 1970

Earlier work this paper cites.

Compilers: Principles techniques and tools. 2007

Alfred V Aho, Monica S Lam, Ravi Sethi, and Jeffrey D Ullman. 2006 · 2006

Earlier work this paper cites.

Improving source code search with natural language phrasal representations of method signatures. In 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011) . IEEE, 524–527

Emily Hill, Lori Pollock, and K Vijay-Shanker. 2011 · 2011

Earlier work this paper cites.

Mining heterogeneous information networks: a structural analysis approach

Yizhou Sun and Jiawei Han. 2013 · 2013

Earlier work this paper cites.

Convolutional neural networks over tree structures for programming language processing. In Proceedings of the AAAI conference on artificial intelligence , Vol. 30

Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. 2016 · 2016

Earlier work this paper cites.

Learning to represent programs with graphs

Original

Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2017 · 2017

Earlier work this paper cites.

IECS: Intent-enforced code search via extended Boolean model

Yangrui Yang and Qing Huang. 2017 · 2017

Earlier work this paper cites.

Neural code comprehension: A learnable representation of code semantics

Tal Ben-Nun, Alice Shoshana Jakobovits, and Torsten Hoefler. 2018 · 2018

Earlier work this paper cites.

Automated software vulnerability detection with machine learning

Original

Jacob A Harer, Louis Y Kim, Rebecca L Russell, Onur Ozdemir, Leonard R Kosta, Akshay Rangamani, Lei H Hamilton, Gabriel I Centeno, Jonathan R Key, Paul M Ellingwood, et al · 2018

Earlier work this paper cites.

code2vec: Learning distributed representations of code

Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019 · 2019

Earlier work this paper cites.

When deep learning met code search. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 964–974

Jose Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, and Satish Chandra. 2019 · 2019

Cited alongside, same era.

Codebert: A pre-trained model for programming and natural languages

Original

Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020

Cited alongside, same era.

Graphcodebert: Pre-training code representations with data flow

Original

Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al · 2020

Cited alongside, same era.

An empirical study on the usage of bert models for code completion. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) . IEEE, 108–119

Matteo Ciniselli, Nathan Cooper, Luca Pascarella, Denys Poshyvanyk, Massimiliano Di Penta, and Gabriele Bavota. 2021 · 2021

Reacc: A retrieval-augmented code completion framework

Original

Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, and Alexey Svyatkovskiy. 2022 · 2022

Later among the works it cites.

Docprompting: Generating code by retrieving the docs

Shuyan Zhou, Uri Alon, Frank F Xu, Zhiruo Wang, Zhengbao Jiang, and Graham Neubig. 2022 · 2022

Later among the works it cites.

GPT-4 Technical Report

Original

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023

Later among the works it cites.

Retrieval-based prompt selection for code-related few-shot learning. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2450–2462

Noor Nashid, Mifta Sintaha, and Ali Mesbah. 2023 · 2023

Later among the works it cites.

Code llama: Open foundation models for code