Code quality assurance system construction
Solver has multiple built-in quality assurance mechanisms, along with the following best practices to ensure code reliability:
- Input Specification::
- Describe the requirement using the "Given-When-Then" format (e.g., "Return 401 status code when user is not logged in").
- Specify boundary conditions (e.g., "memory limit when processing 100,000 records")
- Attachment of existing test cases as validation baseline
- Review process::
- Enable the "-review" parameter to require the generation of a design thinking document.
- Setting up mandatory CI checks (lint/unit tests/integration tests)
- Configure automatic review rules (e.g. complexity > 15 requires manual review)
- fig. repository of knowledge (e.g. scientific knowledge)::
- Using "@learn" to mark good generation cases
- Build a team-exclusive library of prompt templates
- Regular export of quality reports (precision/recall rate indicators)
Real-world example: after 3 months of tuning, an enterprise's AI-generated code has a first-time pass rate of 92%, a 47 percentage point improvement over the initial period.
This answer comes from the articleSolver: the smart tool for autonomously completing programming tasksThe
May not be reproduced without permission:AI productivity tools " How to avoid common quality issues with AI-generated code?
