Test Root Cause Analysis

Test Root Cause Analysis

Code Review streamlines post-coding workflows by automating bug detection, security analysis, and AI-powered fixes directly in GitHub. By integrating seamlessly into developer workflows, it eliminates review bottlenecks and reduces deployment time from days to hours.

Every developer experiences the friction of post-coding workflows—waiting for PR reviews, fixing vulnerabilities flagged by security teams, and ensuring compliance before a feature can go live.

CloudAEye wanted to intervene right at this critical point and make the transition from coding to deployment seamless. To achieve this, Code Review needed to: ✅ Eliminate friction in the review process ✅ Automate bug detection and security analysis ✅ Provide AI-generated fixes to speed up development ✅ Integrate deeply into GitHub to minimize context switching

We studied existing AI-assisted code review tools, analyzing:

How they integrated into GitHub or other version control systems

What kind of reports they generated (security vulnerabilities, code quality insights, etc.)

How developers interacted with these tools within their workflow

This led to a Figma prototype - a jumping board from which we could pivot to a point of better UX.

Once we had a working prototype, we conducted hands-on user testing with a startup founder and engineers, asking them to review a Figma prototype. Their key insights shaped the next stage of our design:

Developers didn't want a separate UI – switching between CloudAEye and GitHub was inefficient

Better integration was needed – surfacing insights directly in GitHub comments was preferable

Report credibility was key – AI-generated reports needed risk classification to be trusted

Our initial prototype surfaced AI-generated code review reports Security vulnerability detection PR summaries

Challenge: Developers didn’t want to switch between GitHub and CloudAEye's UI. The developer is already multitasking, and they didn't want another window open.

Solution: It was clear to us, that for the developer to have an integrated experience wherein the context of the report was close to the report itself

Based on user feedback, we transformed Code Review into a GitHub integration, featuring:

AI-generated comments under pull requests

Automated security analysis & bug detection

A CloudAEye bot that could be invoked on-demand

This ensured developers never had to leave GitHub to access insights.

To boost trust in AI-generated reports, we added:

Categorization using OWASP standards

Risk levels for security vulnerabilities

Priority-based sorting of issues

This made it easier for teams to focus on high-impact issues first.

We successfully built a GitHub-integrated AI code reviewer that: 🚀 Reduced post-coding workflow time from 4 days to under 1 day 💡 Enabled seamless AI-powered bug fixes 🔗 Kept developers entirely within GitHub for their reviews 🔍 Classified security issues using OWASP standards By embedding intelligence directly into developer workflows, Code Review turned slow, manual review cycles into an AI-assisted process, helping teams ship faster and with confidence.