Code Review Use Case

Memfit AI provides intelligent code review capabilities, combining static analysis with AI-powered understanding to identify security vulnerabilities and code quality issues.

Overview

Effective code review requires:

• Understanding of code structure
• Security vulnerability detection
• Best practice validation
• Contextual recommendations

Memfit AI leverages Yaklang's SSA (Static Single Assignment) analysis and SyntaxFlow for comprehensive code review.

Powered by Yaklang SSA

What is SSA?

Static Single Assignment is a compiler intermediate representation that:

• Simplifies data flow analysis
• Enables precise vulnerability tracking
• Supports cross-function analysis
• Provides complete code coverage

SyntaxFlow Integration

SyntaxFlow is Yaklang's DSL for syntax pattern matching:

• Define vulnerability signatures
• Create custom detection rules
• Match complex code patterns
• Trace data flows

Workflow Example

User Request

"Review the Java code in /project/src for security vulnerabilities"

Execution Flow

1. Code Parsing - Parse source code into SSA representation
2. Pattern Matching - Apply SyntaxFlow rules
3. Data Flow Analysis - Track user input to dangerous sinks
4. AI Analysis - Contextual understanding of findings
5. Prioritization - Risk-based ranking
6. Reporting - Detailed findings with recommendations

Key Capabilities

Multi-Language Support

Memfit AI supports code review for:

• Java
• JavaScript/TypeScript
• Python
• PHP
• Go
• And more...

Vulnerability Categories

Detects common vulnerability patterns:

Category	Examples
Injection	SQL, Command, XSS, LDAP
Authentication	Hardcoded credentials, weak crypto
Access Control	Improper authorization
Data Exposure	Sensitive data logging
Configuration	Insecure defaults

Focus Mode: Code Audit

The Code Audit Focus Mode provides:

• Specialized prompts for code analysis
• Automatic pattern library loading
• Context-aware recommendations
• Language-specific best practices

Example Session

User: Review the authentication module in /src/auth

Memfit AI: I'll analyze the authentication module for security issues.

[Parsing code into SSA representation]
[Applying security patterns via SyntaxFlow]

Findings:

1. HIGH: SQL Injection (auth/UserDAO.java:45)
   - User input flows directly to SQL query
   - Recommendation: Use parameterized queries
   
2. MEDIUM: Weak Password Hashing (auth/PasswordService.java:23)
   - Using MD5 for password hashing
   - Recommendation: Use bcrypt or Argon2
   
3. LOW: Verbose Error Messages (auth/LoginController.java:67)
   - Stack traces exposed to users
   - Recommendation: Use generic error messages

Would you like me to explain any finding in detail?

Integration with Forges

Java Code Audit Forge

Specialized for Java applications:

• Spring Security patterns
• OWASP Java rules
• Common Java pitfalls
• Framework-specific checks

Custom Forge Creation

Create organization-specific Forges:

• Internal coding standards
• Compliance requirements
• Custom vulnerability patterns
• Approved library usage

Benefits

Aspect	Benefit
Accuracy	SSA-based analysis reduces false positives
Depth	Data flow tracking finds complex vulnerabilities
Speed	Automated analysis accelerates review
Learning	AI improves with codebase familiarity
Customization	SyntaxFlow enables custom rules