This repo collects and categorizes code-completion-related papers on popular conferences and journals.
Currently the joint table is in progress, and contributions are more than welcomed.
| Title | Publication |
|---|---|
| An Empirical Study on the Usage of Transformer Models for Code Completion | TSE 2022 |
| Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models | CHI 2022 |
| Productivity Assessment of Neural Code Completion | MAPS 2022 |
| An Empirical Study on the Usage of BERT Models for Code Completion | MSR 2021 |
| When Code Completion Fails: Case Study on Real-World Completions | ICSE 2019 |
| Title | Publication |
|---|---|
| Boosting source code suggestion with self-supervised Transformer Gated Highway | JSS 2023 |
| A unified multi-task learning model for AST-level and token-level code completion | EMSE 2022 |
| Next Syntactic-Unit Code Completion and Applications | ASE 2022 |
| All you need is logs: improving code completion by learning from anonymous IDE usage logs | FSE 2022 |
| Language-parametric static semantic code completion | OOPSLA 2022 |
| LEARNING TO COMPLETE CODE WITH SKETCHES | ICLR 2022 |
| CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences | ICSE 2022 |
| Improving Code Autocompletion with Transfer Learning | ICSE (SEIP) 2022 |
| Sequential coding patterns: How to use them effectively in code recommendation | IST 2021 |
| ReACC: A Retrieval-Augmented Code Completion Framework | ACL 2022 |
| Code Prediction by Feeding Trees to Transformers | ICSE 2021 |
| Fast and Memory-Efficient Neural Code Completion | MSR 2021 |
| Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy | EMNLP 2021 |
| Siri, Write the Next Method | ICSE 2021 |
| Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs | AAAI 2021 |
| Learning Autocompletion from Real-World Datasets | ICSE (SEIP) 2021 |
| CodeGRU: Context-aware deep learning with gated recurrent unit for source code modeling | IST 2020 |
| A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning | ICPC 2020 |
| Modeling programs hierarchically with stack-augmented LSTM | JSS 2020 |
| IntelliCode Compose: Code Generation using Transformer | FSE 2020 |
| Multi-task Learning based Pre-trained Language Model for Code Completion | ASE 2020 |
| Combining Program Analysis and Statistical Language Model for Code Statement Completion | ASE 2019 |
| Pythia: AI-assisted Code Completion System | KDD 2019 |
| Code Completion with Neural Attention and Pointer Networks | IJCAI 2018 |
| Stepwise API usage assistance using n-gram language models | JSS 2017 |
| A Language Model for Statements of Software Code | ASE 2017 |
| Are Deep Neural Networks the Best Choice for Modeling Source Code? | FSE 2017 |
| Design annotations to improve API discoverability | JSS 2017 |
| PHOG: Probabilistic Model for Code | ICML 2016 |
| Probabilistic Model for Code with Decision Trees | OOPSLA 2016 |
| Graph-Based Statistical Language Model for Code | ICSE 2015 |
| On the Localness of Software | FSE 2014 |
| Code Completion with Statistical Language Models | PLDI 2014 |
| A Statistical Semantic Language Model for Source Code | FSE 2013 |
| On the Naturalness of Software | ICSE 2012 |
The repo is released under the MIT license.