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Large language models of code (Code-LLMs) have recently brought tremendous advances to code completion, a fundamental feature of programming assistance and code intelligence.
Learning from examples to improve code completion systems
Marcel Bruch, Martin Monperrus, and Mira Mezini · 2009
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A statistical semantic language model for source code
Tung Thanh Nguyen, Anh Tuan Nguyen, Hoan Anh Nguyen, and Tien N Nguyen · 2013
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Reversible debugging software “quantify the time and cost saved using reversible debuggers”
Tom Britton, Lisa Jeng, Graham Carver, and Paul Cheak · 2013
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Mining source code repositories at massive scale using language modeling
Miltiadis Allamanis and Charles Sutton · 2013
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On the localness of software
Zhaopeng Tu, Zhendong Su, and Premkumar Devanbu · 2014
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Mining idioms from source code
Miltiadis Allamanis and Charles Sutton · 2014
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Defects4j: A database of existing faults to enable controlled testing studies for java programs
René Just, Darioush Jalali, and Michael D Ernst · 2014
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This is what your developers are doing 75% of the time, and this is the cost you pay
Ariel Assaraf · 2015
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The manybugs and introclass benchmarks for automated repair of c programs
Claire Le Goues, Neal Holtschulte, Edward K Smith, Yuriy Brun, Premkumar Devanbu, Stephanie Forrest, and Westley Weimer · 2015
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Intelligent code completion with bayesian networks
Sebastian Proksch, Johannes Lerch, and Mira Mezini · 2015
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Suggesting accurate method and class names
Miltiadis Allamanis, Earl T Barr, Christian Bird, and Charles Sutton · 2015
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A study of visual studio usage in practice
Sven Amann, Sebastian Proksch, Sarah Nadi, and Mira Mezini · 2016
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On the naturalness of software
Abram Hindle, Earl T Barr, Mark Gabel, Zhendong Su, and Premkumar Devanbu · 2016
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Neural code completion
Chang Liu, Xin Wang, Richard Shin, Joseph E Gonzalez, and Dawn Song · 2016
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Probabilistic model for code with decision trees
Veselin Raychev, Pavol Bielik, and Martin Vechev · 2016
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Phog: probabilistic model for code
Pavol Bielik, Veselin Raychev, and Martin Vechev · 2016
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Code completion with neural attention and pointer networks
Jian Li, Yue Wang, Michael R Lyu, and Irwin King · 2017
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Deepfix: Fixing common c language errors by deep learning
Rahul Gupta, Soham Pal, Aditya Kanade, and Shirish Shevade · 2017
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Quixbugs: A multi-lingual program repair benchmark set based on the quixey challenge
Derrick Lin, James Koppel, Angela Chen, and Armando Solar-Lezama · 2017
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Learning to represent programs with graphs
Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi · 2017
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A survey of machine learning for big code and naturalness
Miltiadis Allamanis, Earl T Barr, Premkumar Devanbu, and Charles Sutton · 2018
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Generative code modeling with graphs
Marc Brockschmidt, Miltiadis Allamanis, Alexander L Gaunt, and Oleksandr Polozov · 2018
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Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian · 2018
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When code completion fails: A case study on real-world completions
Vincent J Hellendoorn, Sebastian Proksch, Harald C Gall, and Alberto Bacchelli · 2019
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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
Cited alongside, same era.
Re-factoring based program repair applied to programming assignments
Yang Hu, Umair Z Ahmed, Sergey Mechtaev, Ben Leong, and Abhik Roychoudhury · 2019
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Neural program repair by jointly learning to localize and repair
Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, and Rishabh Singh · 2019
Cited alongside, same era.
Global relational models of source code
Vincent J Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber · 2019
Cited alongside, same era.
Sequencer: Sequence-to-sequence learning for end-to-end program repair
Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, and Martin Monperrus · 2019
Cited alongside, same era.
Break-it-fix-it: Unsupervised learning for program repair
Michihiro Yasunaga and Percy Liang · 2021
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Codenet: A large-scale AI for code dataset for learning a diversity of coding tasks
Ruchir Puri, David S Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, and Frederick Reiss · 2021
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Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi · 2021
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Learning to extend program graphs to work-in-progress code, 2021
Xuechen Li, Chris J. Maddison, and Daniel Tarlow · 2021
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Code prediction by feeding trees to transformers. in 2021 ieee/acm 43rd international conference on software engineering (icse), 2021
Seohyun Kim, Jinman Zhao, Yuchi Tian, and Satish Chandra · 2021
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Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi · 2019
Cited alongside, same era.
Learning how to mutate source code from bug-fixes
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk · 2019
Cited alongside, same era.
Deep learning for source code modeling and generation: Models, applications, and challenges
Triet HM Le, Hao Chen, and Muhammad Ali Babar · 2020
Cited alongside, same era.
Multi-task learning based pre-trained language model for code completion
Fang Liu, Ge Li, Yunfei Zhao, and Zhi Jin · 2020
Cited alongside, same era.
Codebleu: a method for automatic evaluation of code synthesis
Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, and Shuai Ma · 2020
Cited alongside, same era.
How often do single-statement bugs occur? the manysstubs4j dataset
Rafael-Michael Karampatsis and Charles Sutton · 2020
Cited alongside, same era.
Structural language models of code
Uri Alon, Roy Sadaka, Omer Levy, and Eran Yahav · 2020
Cited alongside, same era.
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Learning to complete code with sketches
Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, and Miltiadis Allamanis · 2021
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Unified pre-training for program understanding and generation
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
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Semantic bug seeding: a learning-based approach for creating realistic bugs
Jibesh Patra and Michael Pradel · 2021
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Self-supervised bug detection and repair
Miltiadis Allamanis, Henry Jackson-Flux, and Marc Brockschmidt · 2021
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A conversational paradigm for program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2022
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User evaluation of incoder based on statement completion
Marc Otten · 2022
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Fixeval: Execution-based evaluation of program fixes for competitive programming problems
Md Mahim Anjum Haque, Wasi Uddin Ahmad, Ismini Lourentzou, and Chris Brown · 2022
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Review4repair: Code review aided automatic program repairing
Faria Huq, Masum Hasan, Md Mahim Anjum Haque, Sazan Mahbub, Anindya Iqbal, and Toufique Ahmed · 2022
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Can we learn from developer mistakes? learning to localize and repair real bugs from real bug fixes
Cedric Richter and Heike Wehrheim · 2022
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Incoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis · 2022
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Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
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On distribution shift in learning-based bug detectors
Jingxuan He, Luca Beurer-Kellner, and Martin Vechev · 2022
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Productivity assessment of neural code completion
Albert Ziegler, Eirini Kalliamvakou, X Alice Li, Andrew Rice, Devon Rifkin, Shawn Simister, Ganesh Sittampalam, and Edward Aftandilian · 2022
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ReACC: A retrieval-augmented code completion framework
Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, and Alexey Svyatkovskiy · 2022
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Heat: Hyperedge attention networks
Dobrik Georgiev, Marc Brockschmidt, and Miltiadis Allamanis · 2022
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Learning realistic mutations: Bug creation for neural bug detectors
Cedric Richter and Heike Wehrheim · 2022
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Semantic robustness of models of source code
Jordan Henkel, Goutham Ramakrishnan, Zi Wang, Aws Albarghouthi, Somesh Jha, and Thomas Reps · 2022
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Backdoors in neural models of source code
Goutham Ramakrishnan and Aws Albarghouthi · 2022
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Better context makes better code language models: A case study on function call argument completion
Hengzhi Pei, Jinman Zhao, Leonard Lausen, Sheng Zha, and George Karypis · 2023
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