Source Code Analysis Tools: How Rafter Finds Vulnerabilities Fast
Written by the Rafter Team
Source code analysis tools examine your codebase line by line, tracing data flows and control paths to find security vulnerabilities before they ship. The challenge is not detection — it is precision. Legacy scanners bury teams in hundreds of false positives. Rafter takes a different approach: context-aware analysis that prioritizes real, exploitable flaws and delivers actionable fix suggestions.
Teams that rely on periodic manual audits instead of continuous source code analysis miss an average of 45 days between vulnerability introduction and detection. Automated analysis on every commit closes that gap to minutes.
Try Rafter's source code analysis free — results on your first commit.
What Source Code Analysis Tools Do
A source code analysis tool parses your code into an abstract syntax tree, then applies a combination of pattern matching, taint tracking, and control flow analysis. It identifies where untrusted data enters your application, follows it through every transformation and function call, and flags locations where it reaches a dangerous operation — a database query, a shell command, an HTML template — without proper sanitization.
The best tools go further. They resolve cross-file references, understand framework-specific conventions (middleware chains, ORM query builders, template engines), and differentiate between validated and unvalidated data paths. This context is what separates a useful finding from noise.
How Rafter's Source Code Analysis Works
Rafter performs multi-pass analysis across your entire repository, not just the changed files in a pull request. This matters because a new function might introduce a vulnerability only when called from an existing code path three files away.
The scanning pipeline runs in three stages:
Structural analysis parses every file, builds a cross-file dependency graph, and identifies entry points — API routes, event handlers, CLI commands — where external data enters the application.
Taint propagation tracks untrusted data from each entry point through assignments, function calls, and returns. It understands sanitization functions and marks data as clean when it passes through validated transformations. Data that reaches a sensitive sink without sanitization triggers a finding.
Contextual prioritization scores each finding based on exploitability. A SQL injection reachable from a public API endpoint ranks higher than one behind authentication and rate limiting. This ranking lets teams fix the most dangerous issues first instead of triaging a flat list.
Rafter delivers findings directly in your pull request with contextual fix suggestions — not just "vulnerability found" but a specific code change that resolves it. Developers fix issues in the same workflow where they write code.
Source Code Analysis Tools vs. Other Approaches
Source code analysis operates at build time, examining code without executing it. This makes it fast, comprehensive, and safe to run in CI. But it has blind spots — runtime configuration issues, business logic flaws, and environment-specific vulnerabilities require dynamic testing.
The strongest security posture layers source code analysis with dependency scanning (catching known CVEs in third-party libraries), secrets detection (finding leaked credentials), and periodic penetration testing. Rafter combines the first three in a single automated scan on every commit, so your team only needs to coordinate pen tests separately.
Getting Started
Add Rafter to your repository and push a commit. The first scan analyzes your full codebase and returns prioritized findings within minutes. Subsequent scans run incrementally on changed files while maintaining full cross-file context.
Start source code analysis with Rafter — no configuration files required.