Papers with text-to-code generation
Compilable Neural Code Generation with Compiler Feedback (2022.findings-acl)
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Xin Wang, Yasheng Wang, Yao Wan, Fei Mi, Yitong Li, Pingyi Zhou, Jin Liu, Hao Wu, Xin Jiang, Qun Liu
| Challenge: | Existing deep-learning approaches model code generation as text generation, but few of them account for compilability of the generated programs. |
| Approach: | They propose a three-stage pipeline utilizing compiler feedback for compilable code generation to improve compilability. |
| Outcome: | The proposed pipeline improves compilability of generated programs by combining compiler feedback, language model fine-tuning, and compilable discrimination. |
Text-to-Code Generation with Modality-relative Pre-training (2024.eacl-long)
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| Challenge: | Large pre-trained language models have been applied to programming language tasks with great success, often through further pre-training of a strictly-natural language model. |
| Approach: | They propose to map programming language modalities into the same embedding space by separating embeddable spaces between modality and modality-relative training objectives. |
| Outcome: | The proposed model can be adapted and represented differently depending on which modality they belong to and to the ultimate benefit of the downstream task. |
Code Execution with Pre-trained Language Models (2023.findings-acl)
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Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, Nan Duan
| Challenge: | Pre-trained code intelligence models ignore the execution trace and only rely on source code and syntactic structures to understand code execution. |
| Approach: | They develop a mutation-based data augmentation technique to create a Python dataset and task for code execution that challenges existing models. |
| Outcome: | The proposed model outperforms existing models on code execution and shows its potential for zero-shot code-to-code search and text-to code generation. |