Code Planner, one of the most groundbreaking features of Rovo Dev Agent, redefines the requirements-to-code implementation path. This feature is automatically activated when a developer creates or opens an Issue in Jira for intelligent multi-dimensional analysis.
The system first parses business information such as requirement descriptions and acceptance criteria in Jira tasks, and also scans the technology stack (including programming languages, frameworks and architectural patterns used) in the code repository. More importantly, it retrieves the associated Confluence documents to build a complete contextual understanding. Based on this data, the AI engine generates a technical solution that contains the following elements:
- List of specific code files to be modified
- Suggested locations and modalities for changes
- Impact assessment of relevant dependencies
- Description of potential boundary conditions
The solution is presented directly in the Jira interface, providing developers with an immediate starting point for coding. Test data shows that this automated planning reduces requirements understanding time by approximately 40%. Particularly valuable for developers new to the project, it significantly reduces the time required to familiarize themselves with the existing code base.
AI-generated planning also ensures consistency with established code styles and architectural patterns, which helps maintain the overall quality of the code compared to traditional manual technical solution writing.
This answer comes from the articleRovo Dev Agent: Artificial Intelligence Development Agent Tool from AtlassianThe
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