ClaudeAI Sonnet 3.7 helped to Refactored Complex monolithic code written for AI AGENT (500 lines of code) into modularize classes by group together similar methods in a class.
Also, helped to implement Gitlab Pipeline status tracking to track progress where the execution reached for AI Agent & what is next to trigger
1. The AI agent clones the repository
2. The agent runs test coverage (code lines glowing green/red, representing covered/uncovered sections).
3. Uncovered lines are highlighted in red, extracted for further processing.
4. A futuristic LLM system generates new test cases dynamically (represented as an AI brain connecting to code snippets).
5. The newly suggested test cases are executed (pass/fail indicators).
6. If passed, commits are pushed (depicted as a commit message with green checkmarks).
7. If failed, the LLM iterates, refining tests until successful.
8. Final successful test cases are pushed back to the repository.
9. Create a Merge Request to the Repo for pushed changes related to newly added test cases
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