Cloud software code generation via knowledge graphs and multi-modal learning
Abstract As cloud computing continues to experience rapid growth, the demand for cloud-native applications is escalating, leading to more complex and diverse development requirements. In this context, automated code generation plays a pivotal role in accelerating cloud-native application development...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-025-00758-5 |
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| _version_ | 1849238496545341440 |
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| author | Fanlong Zhang Huanming Chen Quan Chen Jianqi Liu |
| author_facet | Fanlong Zhang Huanming Chen Quan Chen Jianqi Liu |
| author_sort | Fanlong Zhang |
| collection | DOAJ |
| description | Abstract As cloud computing continues to experience rapid growth, the demand for cloud-native applications is escalating, leading to more complex and diverse development requirements. In this context, automated code generation plays a pivotal role in accelerating cloud-native application development. However, existing studies often overlook the full potential of leveraging multiple program representations, typically focusing on a single form derived from well-structured programs. In this paper, we demonstrate that integrating multiple program representations, specifically, code snippets and their associated ASTs can significantly enhance code generation for cloud-native applications. We construct a code knowledge graph (CodeKG) to retrieve related programs based on textual and structural similarities, enabling richer contextual information for generation. To realize this integration, we design a framework that employs shared-retentive networks (shared-RetNet) and an AST-based Transformer to extract and align features from natural language, code tokens, and ASTs. By applying contrastive loss and cross-attention mechanisms, our method effectively fuses diverse modalities to strengthen code generation performance. Experiments on two processed open-source datasets demonstrate that our method, without relying on extensive pre-training, achieves superior results compared to existing pretrained models across BLEU-4, CodeBLEU, and ROUGE-L metrics, paving the way for more efficient and intelligent cloud-native software development. |
| format | Article |
| id | doaj-art-de5210d36c2047cbb0d25c356ddb9c68 |
| institution | Kabale University |
| issn | 2192-113X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj-art-de5210d36c2047cbb0d25c356ddb9c682025-08-20T04:01:35ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2025-07-0114111910.1186/s13677-025-00758-5Cloud software code generation via knowledge graphs and multi-modal learningFanlong Zhang0Huanming Chen1Quan Chen2Jianqi Liu3School of Computer Science and Technology, Guangdong University of TechnologySchool of Computer Science and Technology, Guangdong University of TechnologySchool of Computer Science and Technology, Guangdong University of TechnologySchool of Computer Science and Technology, Guangdong University of TechnologyAbstract As cloud computing continues to experience rapid growth, the demand for cloud-native applications is escalating, leading to more complex and diverse development requirements. In this context, automated code generation plays a pivotal role in accelerating cloud-native application development. However, existing studies often overlook the full potential of leveraging multiple program representations, typically focusing on a single form derived from well-structured programs. In this paper, we demonstrate that integrating multiple program representations, specifically, code snippets and their associated ASTs can significantly enhance code generation for cloud-native applications. We construct a code knowledge graph (CodeKG) to retrieve related programs based on textual and structural similarities, enabling richer contextual information for generation. To realize this integration, we design a framework that employs shared-retentive networks (shared-RetNet) and an AST-based Transformer to extract and align features from natural language, code tokens, and ASTs. By applying contrastive loss and cross-attention mechanisms, our method effectively fuses diverse modalities to strengthen code generation performance. Experiments on two processed open-source datasets demonstrate that our method, without relying on extensive pre-training, achieves superior results compared to existing pretrained models across BLEU-4, CodeBLEU, and ROUGE-L metrics, paving the way for more efficient and intelligent cloud-native software development.https://doi.org/10.1186/s13677-025-00758-5Cloud computingSoftware engineeringCode generationMulti-modal learning |
| spellingShingle | Fanlong Zhang Huanming Chen Quan Chen Jianqi Liu Cloud software code generation via knowledge graphs and multi-modal learning Journal of Cloud Computing: Advances, Systems and Applications Cloud computing Software engineering Code generation Multi-modal learning |
| title | Cloud software code generation via knowledge graphs and multi-modal learning |
| title_full | Cloud software code generation via knowledge graphs and multi-modal learning |
| title_fullStr | Cloud software code generation via knowledge graphs and multi-modal learning |
| title_full_unstemmed | Cloud software code generation via knowledge graphs and multi-modal learning |
| title_short | Cloud software code generation via knowledge graphs and multi-modal learning |
| title_sort | cloud software code generation via knowledge graphs and multi modal learning |
| topic | Cloud computing Software engineering Code generation Multi-modal learning |
| url | https://doi.org/10.1186/s13677-025-00758-5 |
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