New Deep Learning-Based Approach for Source Code Generation: Application to Computer Vision Systems
Deep learning has enabled significant progress in source code generation, aiming to reduce the manual, error-prone, and time-consuming aspects of software development. While many existing models rely on recurrent neural networks (RNNs) with sequence-to-sequence architectures, these approaches strugg...
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| Format: | Article |
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MDPI AG
2025-07-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/6/7/162 |
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| author | Wafa Alshehri Salma Kammoun Jarraya Arwa Allinjawi |
| author_facet | Wafa Alshehri Salma Kammoun Jarraya Arwa Allinjawi |
| author_sort | Wafa Alshehri |
| collection | DOAJ |
| description | Deep learning has enabled significant progress in source code generation, aiming to reduce the manual, error-prone, and time-consuming aspects of software development. While many existing models rely on recurrent neural networks (RNNs) with sequence-to-sequence architectures, these approaches struggle with the long and complex token sequences typical in source code. To address this, we propose a grammar-based convolutional neural network (CNN) combined with a tree-based representation to enhance accuracy and efficiency. Our model achieves state-of-the-art results on the benchmark HEARTHSTONE dataset, with a BLEU score of 81.4 and an Acc+ of 62.1%. We further evaluate the model on our proposed dataset, AST2CVCode, designed for computer vision applications, achieving 86.2 BLEU and 51.9% EM. Additionally, we introduce BLEU+, an enhanced evaluation metric tailored for functional correctness in code generation, which achieves a BLEU+ score of 92.0% on the AST2CVCode dataset. These results demonstrate the effectiveness of our approach in both model architecture and evaluation methodology. |
| format | Article |
| id | doaj-art-eef26880431048f58706e3ad9935eacd |
| institution | DOAJ |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-eef26880431048f58706e3ad9935eacd2025-08-20T03:13:39ZengMDPI AGAI2673-26882025-07-016716210.3390/ai6070162New Deep Learning-Based Approach for Source Code Generation: Application to Computer Vision SystemsWafa Alshehri0Salma Kammoun Jarraya1Arwa Allinjawi2Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDeep learning has enabled significant progress in source code generation, aiming to reduce the manual, error-prone, and time-consuming aspects of software development. While many existing models rely on recurrent neural networks (RNNs) with sequence-to-sequence architectures, these approaches struggle with the long and complex token sequences typical in source code. To address this, we propose a grammar-based convolutional neural network (CNN) combined with a tree-based representation to enhance accuracy and efficiency. Our model achieves state-of-the-art results on the benchmark HEARTHSTONE dataset, with a BLEU score of 81.4 and an Acc+ of 62.1%. We further evaluate the model on our proposed dataset, AST2CVCode, designed for computer vision applications, achieving 86.2 BLEU and 51.9% EM. Additionally, we introduce BLEU+, an enhanced evaluation metric tailored for functional correctness in code generation, which achieves a BLEU+ score of 92.0% on the AST2CVCode dataset. These results demonstrate the effectiveness of our approach in both model architecture and evaluation methodology.https://www.mdpi.com/2673-2688/6/7/162software requirementssource code generationdeep learning |
| spellingShingle | Wafa Alshehri Salma Kammoun Jarraya Arwa Allinjawi New Deep Learning-Based Approach for Source Code Generation: Application to Computer Vision Systems AI software requirements source code generation deep learning |
| title | New Deep Learning-Based Approach for Source Code Generation: Application to Computer Vision Systems |
| title_full | New Deep Learning-Based Approach for Source Code Generation: Application to Computer Vision Systems |
| title_fullStr | New Deep Learning-Based Approach for Source Code Generation: Application to Computer Vision Systems |
| title_full_unstemmed | New Deep Learning-Based Approach for Source Code Generation: Application to Computer Vision Systems |
| title_short | New Deep Learning-Based Approach for Source Code Generation: Application to Computer Vision Systems |
| title_sort | new deep learning based approach for source code generation application to computer vision systems |
| topic | software requirements source code generation deep learning |
| url | https://www.mdpi.com/2673-2688/6/7/162 |
| work_keys_str_mv | AT wafaalshehri newdeeplearningbasedapproachforsourcecodegenerationapplicationtocomputervisionsystems AT salmakammounjarraya newdeeplearningbasedapproachforsourcecodegenerationapplicationtocomputervisionsystems AT arwaallinjawi newdeeplearningbasedapproachforsourcecodegenerationapplicationtocomputervisionsystems |