My First An AI-powered agent automating test coverage prototype for GitHub/GitLab repositories (Next video will showcase the end to end working flow with increasing coverage for a gitlab/github repo)
"Every software project has one BIG problem—low test coverage that's often leads to unexpected bugs in production! But WHY does it happen?"
Based on my experience in last 20 years, There 3 Major Reasons (there can be many more but my inclination more towards on technical aspects ;-))
Reason1 - Adding new features, enhancing existing feature, or fixing bugs introduces uncovered lines
Reason2 - Tight deadlines lead to incomplete test suites
Reason3 - Developers miss edge cases
To solve this problem, I thought to take this as first real time problem to build solution so probability of bugs can be reduced with increased test coverage
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.
"This means fewer bugs, more confidence, and faster development!"
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