Beyond Snippet Assistance: A Workflow-Centric Framework for End-to-End AI-Driven Code Generation
Recent AI-assisted coding tools, such as GitHub Copilot and Cursor, have enhanced developer productivity through real-time snippet suggestions. However, these tools primarily assist with isolated coding tasks and lack a structured approach to automating complex, multi-step software development workf...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/14/3/94 |
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| Summary: | Recent AI-assisted coding tools, such as GitHub Copilot and Cursor, have enhanced developer productivity through real-time snippet suggestions. However, these tools primarily assist with isolated coding tasks and lack a structured approach to automating complex, multi-step software development workflows. This paper introduces a workflow-centric AI framework for end-to-end automation, from requirements gathering to code generation, validation, and integration, while maintaining developer oversight. Key innovations include automatic context discovery, which selects relevant codebase elements to improve LLM accuracy; a structured execution pipeline using Prompt Pipeline Language (PPL) for iterative code refinement; self-healing mechanisms that generate tests, detect errors, trigger rollbacks, and regenerate faulty code; and AI-assisted code merging, which preserves manual modifications while integrating AI-generated updates. These capabilities enable efficient automation of repetitive tasks, enforcement of coding standards, and streamlined development workflows. This approach lays the groundwork for AI-driven development that remains adaptable as LLM models advance, progressively reducing the need for human intervention while ensuring code reliability. |
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| ISSN: | 2073-431X |