AI Code Auditor

Professional development teams spend up to 25% of their week on code review, and that number is climbing — teams with heavy AI-generated code adoption have seen review time increase by over 90%, as more code gets written faster than humans can manually check it. Worse, even careful manual review misses roughly 40% of critical defects, meaning security flaws and logic bugs routinely slip through to production, where they're far more expensive to fix. Existing tools don't close this gap. Traditional linters (pylint, bandit) catch surface-level style and syntax issues but miss deeper security context — privilege escalation patterns, insecure deserialization, business-logic errors — and even when they do flag something, they simply leave a warning for the developer to interpret and fix manually. AI Code Auditor solves this by combining static analysis with IBM Bob 2.0's full-repository context. The system ingests an entire codebase, then dispatches two parallel subagents — a Security Subagent and a Logic Subagent — that scan every file simultaneously rather than sequentially. Findings are cross-referenced against the OWASP ASVS standard, giving structured, industry-aligned detection instead of ad-hoc pattern matching. Results are aggregated into a Report Generator that exports SARIF and OSCAL formats, making findings machine-readable and CI/CD-compatible out of the box. What makes this different from a typical scanner is the final stage: an Actionable Dashboard that doesn't just flag problems — it generates real fixes. Using an actor-critic workflow, one pass proposes a remediation for each finding, and a second pass critiques that fix against the original vulnerability class before presenting it, so developers get a vetted, ready-to-review patch rather than just a warning. Target users are professional development teams and engineering leads who want to shrink code review time without sacrificing thoroughness — plugging into their existing repo-based workflow rather than replacing it. A developer points the tool at their repository, gets a categorized findings dashboard within moments, and can generate and review a proposed fix with one click, closing the loop from detection to remediation in a single tool. This is efficient because it parallelizes what review normally does sequentially, and effective because it goes one step further than any existing tool by producing an actual fix, not just an alert — directly attacking the two root problems: review takes too long, and even when it doesn't, things still get missed.