Auto-Fix Feature Roadmap

Last Updated: November 14, 2025 (17:35 CET) Strategic Decision: AI-Suggested Fixes with Progressive Automation Status: Phase 1 Week 1 - COMPLETE ✅

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Executive Summary

STRATEGIC PIVOT: After analyzing risks and competitive landscape, CodeSlick will implement a phased approach to auto-fixing security vulnerabilities, starting with AI-suggested fixes (human-reviewed) before progressing to full automation.

Why This Approach: - ✅ Aligns with current CodeSlick UX (analyze + on-demand fixes) - ✅ Minimizes liability risk (developer reviews before applying) - ✅ Delivers immediate value (10x faster than manual research) - ✅ Validates market demand (measure acceptance rates) - ✅ Builds confidence progressively (data-driven expansion)

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⚠️ User Responsibilities & Risk Acknowledgment

CRITICAL NOTICE: AI-generated code suggestions are provided as assistance tools only. By using CodeSlick's AI-suggested fixes feature, you acknowledge and accept the following responsibilities and limitations.

Understanding AI-Generated Code

What AI Can Do: - ✅ Generate syntactically correct code based on patterns - ✅ Suggest common security fix patterns (parameterized queries, input sanitization) - ✅ Detect framework/library usage to provide contextual fixes - ✅ Explain security vulnerabilities and remediation approaches - ✅ Save research time by proposing fix implementations

What AI CANNOT Guarantee: - ❌ Full understanding of your application's business logic - ❌ Knowledge of your specific architecture, dependencies, or constraints - ❌ Awareness of edge cases unique to your codebase - ❌ 100% accuracy in all contexts and scenarios - ❌ Freedom from introducing new bugs or breaking existing functionality

Your Responsibilities as a Developer

Before Applying ANY AI-Suggested Fix:

1. Code Review (MANDATORY)

You MUST review every suggested fix and verify:
├── Does this fix address the actual vulnerability?
├── Will this break existing functionality?
├── Are there edge cases the AI didn't consider?
├── Is this compatible with our architecture?
├── Does this follow our team's coding standards?
└── Have I tested this change locally?

2. Contextual Understanding (CRITICAL)

The AI has limited context about:
├── Your database schema and relationships
├── Your authentication/authorization logic
├── Your business rules and validation requirements
├── Your external service integrations
├── Your testing and deployment processes
└── Your team's architectural decisions

YOU must verify the fix works in YOUR specific context.

3. Testing Requirements (NON-NEGOTIABLE)

Before merging ANY AI-suggested fix:
├── Write unit tests for the fixed code
├── Run existing test suite (all tests must pass)
├── Test edge cases manually
├── Verify no regression in related features
├── Review changes with at least one teammate
└── Test in staging environment before production

What You MUST Check Before Applying a Fix

Security Fixes Checklist:

For SQL Injection Fixes:
☐ Does the parameterized query work with our ORM/database?
☐ Are all user inputs properly escaped?
☐ Did we maintain the same query logic?
☐ Are there other similar queries we should fix?

For XSS/Input Sanitization Fixes:
☐ Does the sanitization preserve required HTML?
☐ Is the sanitization applied at the right layer?
☐ Are all output contexts covered?
☐ Does this break any rich-text features?

For Authentication/Authorization Fixes:
☐ Does this maintain our auth flow?
☐ Are all permission checks correct?
☐ Did we consider all user roles?
☐ Does this create new security holes?

For Cryptography Fixes:
☐ Is the algorithm appropriate for our use case?
☐ Are we handling keys securely?
☐ Does this meet our compliance requirements?
☐ Is backward compatibility needed?

When NOT to Apply a Fix

Do NOT apply an AI-suggested fix if:

❌ You don't fully understand what the fix does
❌ The fix modifies critical authentication/authorization code
❌ The fix touches payment or financial logic
❌ The fix changes database migrations or schema
❌ The fix modifies third-party integrations
❌ You haven't tested it locally
❌ You're unsure about business logic impact
❌ The fix seems too complex or "magical"
❌ You're under time pressure (rushed reviews miss bugs)
❌ Tests are failing after applying the fix

Instead: - Research the vulnerability manually - Consult with senior developers - Write the fix yourself with AI suggestion as reference - Request a security audit for critical code

Liability Disclaimer

LEGAL NOTICE:

CodeSlick provides AI-generated code suggestions "AS IS" without warranty
of any kind, express or implied, including but not limited to:

- Warranties of merchantability
- Fitness for a particular purpose
- Non-infringement
- Accuracy or completeness
- Freedom from errors or bugs

YOU ARE SOLELY RESPONSIBLE FOR:
├── Reviewing all AI-generated code before use
├── Testing fixes in your specific environment
├── Ensuring fixes don't break existing functionality
├── Compliance with security/regulatory requirements
├── Any consequences of applying suggested fixes
└── Damages resulting from use of AI-generated code

By using this feature, you agree that:
├── CodeSlick is NOT liable for code quality issues
├── CodeSlick is NOT liable for security vulnerabilities introduced
├── CodeSlick is NOT liable for production outages or data loss
├── CodeSlick is NOT liable for business logic errors
└── You have reviewed and accept our Terms of Service

If you do not accept these terms, DO NOT use AI-suggested fixes.

Best Practices for Safe AI-Fix Usage

1. Incremental Application

Start small:
├── Apply 1-2 fixes first, test thoroughly
├── Observe in production for 24-48 hours
├── If stable, gradually increase usage
└── Never apply 10+ fixes at once without review

2. Version Control Discipline

Always:
├── Create a feature branch for fixes
├── Apply one fix per commit (easy to revert)
├── Write descriptive commit messages
├── Request peer review before merging
└── Have a rollback plan ready

3. Documentation

For each applied fix, document:
├── Why this fix was needed
├── What was changed and why
├── What testing was performed
├── Any edge cases to monitor
└── Who reviewed the change

4. Monitoring Post-Deployment

After deploying fixes:
├── Monitor error rates (should not increase)
├── Watch performance metrics
├── Check user reports/support tickets
├── Review application logs
└── Be ready to rollback if issues appear

Reporting Issues

If an AI-suggested fix causes problems:

1. Immediately: Rollback the change
2. Document: What fix was applied, what broke, reproduction steps
3. Report: Use "Report Bad Fix" button in CodeSlick dashboard
4. Share: Provide code context (sanitized, no secrets)
5. Help us improve: Accurate reports improve AI quality for everyone

Support Channels: - GitHub Issues: https://github.com/codeslick/feedback - Email: support@codeslick.dev - Security Issues: codeslick@codeslick.dev (mark subject: SECURITY)

Summary: Your Obligations

As a professional developer using AI-assisted code:

✅ YOU are the expert, AI is the assistant
✅ YOU own the code quality and correctness
✅ YOU must understand every line you merge
✅ YOU are responsible for testing
✅ YOU accept the risks of AI-generated code
✅ YOU will review changes with your team
✅ YOU will monitor production after deployment

CodeSlick helps you move faster, but YOU are the driver.
Never blindly trust AI suggestions. Always review, test, verify.

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The Auto-Fix Spectrum

What CodeSlick Currently Does

Main App (/ - Monaco Editor): - ✅ Analyze code for 79+ security vulnerabilities - ✅ "Generate Fix" button per issue (AI, 5-10s) - ✅ "Fix All" button (syntax only: semicolons, brackets, var→const)

GitHub PR Integration (Phase 7): - ✅ Automated PR analysis via webhook - ✅ Comment with vulnerabilities found - ❌ No fix suggestions (current gap)

What We're Building (This Roadmap)

Auto-Fix PR Feature Evolution:

Phase 1: AI-Suggested Fixes (Nov 2025)
├── Post PR comment with vulnerability analysis
├── Include AI-generated fix suggestions for each issue
├── Show diff preview for each suggestion
├── One-click "Apply This Fix" button
└── Developer reviews → applies → commits

Phase 2: Tiered Automation (Dec 2025)
├── Tier 1: Auto-fix syntax (no review needed)
├── Tier 2: Suggested fixes for security (one-click apply)
└── Measure acceptance rates → decide on Tier 1 expansion

Phase 3: Smart Auto-Fix (Q1 2026)
├── High-confidence fixes applied automatically
├── Low-confidence fixes remain as suggestions
└── ML model trained on acceptance/rejection data

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Phase 1: AI-Suggested Fixes (2-3 Weeks)

Timeline: November 14 - December 5, 2025

Goal: Enable developers to apply AI-generated security fixes with one click, while maintaining human oversight.

Week 1: Core Implementation ✅ COMPLETE

Status: Completed November 14, 2025

Deliverables: 1. ✅ Update FixApplier - Generate suggestions instead of applying fixes 2. ✅ AI Fix Generation - Use Together AI to generate contextual fixes 3. ⏳ Update PR Comment Formatter - Show suggestions with diff previews (Next) 4. ⏳ Create Apply Fix API - POST /api/teams/[id]/apply-fix (Next)

Technical Changes:

// Before:
FixApplier.applyFixes(input)
  → Returns fixed code (applied automatically)

// After:
FixApplier.generateSuggestions(input)
  → Returns array of suggestions (user chooses)

Suggestion Structure:

{
  id: "fix-sql-injection-line-42",
  vulnerability: {
    type: "SQL Injection",
    line: 42,
    severity: "high",
    cvssScore: 9.1
  },
  currentCode: "const query = `SELECT * FROM users WHERE id = ${userId}`;",
  suggestedFix: "const query = 'SELECT * FROM users WHERE id = ?';\ndb.query(query, [userId]);",
  explanation: "Use parameterized queries to prevent SQL injection attacks",
  diffPreview: "- const query = `SELECT * FROM users WHERE id = ${userId}`;\n+ const query = 'SELECT * FROM users WHERE id = ?';\n+ db.query(query, [userId]);",
  confidence: "high", // AI confidence: high, medium, low
  framework: "express-mysql2" // Detected framework context
}

Week 2-3: Testing & Iteration

Internal Testing (Days 6-7): - Create test PRs with 10+ known vulnerabilities - Verify suggestions are correct - Test "Apply Fix" workflow - Measure time to apply fixes

Beta User Testing (Days 8-10): - Recruit 5-10 developers - Provide test repository with vulnerabilities - Observe via screen share - Critical Questions: - Would you apply this fix? Why/why not? - How would you modify this fix? - Do you trust this suggestion? - Does this save you time?

Iteration (Days 11-15): - Fix UX friction points - Improve AI prompts for low-quality suggestions - Polish UI/copy - Add batch "Apply All" option if needed

Success Criteria (Phase 1)

Quantitative Metrics:

Target (Beta Testing):
├── Suggestion Acceptance Rate: >70%
├── Time to Apply 10 Fixes: <3 minutes
├── Fix Accuracy (user-reported): >90%
├── Bad Fix Rate: <5%
└── Users Who Apply ≥1 Fix: >80%

Qualitative Metrics: - ✅ Beta users report "saved hours of work" - ✅ Zero critical bugs from applied fixes - ✅ Users understand how to review fixes - ✅ Positive feedback on fix quality

Decision Point: - If metrics met → Proceed to Phase 2 - If metrics not met → Iterate on fix quality, re-test

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Phase 2: Tiered Automation (Month 2)

Timeline: December 2025

Goal: Enable automatic fixing for low-risk patterns while keeping security fixes as suggestions.

The Three-Tier Model

Tier 1: Auto-Fix (No Review Required)

Scope: Syntax errors and code quality
Examples:
  ✅ Missing semicolons
  ✅ Unclosed brackets/parentheses
  ✅ var → const conversions
  ✅ Missing await keywords

Risk: Zero (cannot break functionality)
User Experience: Applied automatically, shown in PR

Tier 2: Suggested Fixes (One-Click Apply)

Scope: Security vulnerabilities
Examples:
  💡 SQL injection → parameterized queries
  💡 XSS → input sanitization
  💡 Weak crypto → crypto.randomBytes()
  💡 Path traversal → path.normalize()

Risk: Medium (could affect functionality if context wrong)
User Experience: Developer reviews diff → clicks Apply

Tier 3: Analysis Only (Future)

Scope: Complex architecture issues
Examples:
  📋 Insecure authentication flow
  📋 Missing rate limiting
  📋 Broad CORS policy

Risk: High (requires architecture understanding)
User Experience: Explained with recommendations, manual fix

Implementation Plan

Week 1: Enable Tier 1 Auto-Fixes - Identify patterns with 95%+ acceptance rate from Phase 1 - Implement automatic application for these patterns - Add clear PR comment: "✅ Auto-fixed 5 syntax issues"

Week 2: Refine Tier 2 Suggestions - Improve AI prompts based on Phase 1 feedback - Add framework detection (Express, NestJS, etc.) - Add database detection (MySQL, Postgres, MongoDB)

Week 3-4: Measurement Period - Track acceptance rates by vulnerability type - Identify high-confidence patterns - Measure: Do users modify suggestions before applying?

Decision Criteria for Tier Promotion

Promote Tier 2 → Tier 1 if:

Criteria (measured over 100+ suggestions):
├── Acceptance Rate: >95%
├── Modification Rate: <5% (applied as-is)
├── Rejection Rate: <2%
├── Bad Fix Reports: 0
└── User Trust: Survey shows >90% comfort with auto-apply

Example candidates for promotion:

// Pattern: SQL Injection (Parameterized Queries)
// If 98% of suggestions accepted without modification
// → Promote to Tier 1 (auto-fix)

// Pattern: XSS (Input Sanitization)
// If 85% accepted but 15% modified
// → Keep in Tier 2 (suggestions)

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Phase 3: Smart Auto-Fix (Q1 2026)

Timeline: January - March 2026

Goal: Use ML to predict which fixes are safe to auto-apply vs suggest.

Confidence Scoring System

How It Works:

For each vulnerability:
1. AI generates fix
2. ML model scores confidence (0-100%)
3. Route based on score:
   - >95% → Auto-apply (Tier 1)
   - 70-95% → Suggest (Tier 2)
   - <70% → Analysis only (Tier 3)

Confidence Factors:

Confidence = f(
  pattern_match_strength,      # How well it matches known patterns
  framework_detection_certainty, # Are we sure about Express vs NestJS?
  codebase_context,             # Similar fixes in same repo
  user_acceptance_history,      # Past acceptance rate for this pattern
  code_complexity_score         # Simple vs complex code
)

ML Training Data

Sources: - Phase 1-2 acceptance/rejection data - User modifications to suggestions - Fix types by framework/database - Code complexity metrics - User feedback ratings

Training Goals:

Model Accuracy Targets:
├── Predict acceptance: >90% accuracy
├── False positive rate: <5% (suggesting bad fixes)
├── False negative rate: <10% (missing good fixes)
└── Calibration: High confidence = actually high quality

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Competitive Analysis

Current Market (November 2025)

Snyk: - ✅ Detects vulnerabilities - ✅ Explains issues - ✅ Shows examples from docs - ❌ No AI fix generation - ❌ No one-click apply

CodeRabbit: - ✅ PR analysis - ✅ Code review comments - ❌ No security-focused fixes - ❌ Generic suggestions

Qodo (formerly CodiumAI): - ✅ Test generation - ✅ Code explanations - ❌ No security fix suggestions

GitHub Copilot: - ✅ Code completions - ❌ No vulnerability detection - ❌ No security-focused fixes

CodeSlick's Moat

Unique Combination:

CodeSlick = Security Analysis + AI Fix Generation + One-Click Apply

Moat Components:
1. Security expertise (79+ vulnerability types)
2. Contextual AI (framework + database aware)
3. One-click workflow (faster than copy-paste)
4. Progressive trust (suggestions → auto-fix)
5. Learning system (ML-powered confidence)

Why This is Defensible: - Requires security domain expertise (years to build) - Requires high-quality AI prompts (iterative refinement) - Requires framework detection (complex parsing) - Network effects (more usage = better ML model)

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Business Model Implications

Pricing Tiers (Proposed)

Free Tier: - ✅ Vulnerability detection (79+ checks) - ✅ Tier 1 auto-fixes (syntax only) - ❌ No AI security fix suggestions - Value: Good for individual developers, hobbyists

Team Plan ($99/month): - ✅ All Free features - ✅ Tier 2 suggestions (10 fixes/month) - ✅ One-click apply - ✅ 5 repositories - Value: Small teams, startups

Enterprise Plan ($299/month): - ✅ All Team features - ✅ Unlimited fix suggestions - ✅ Priority support - ✅ Unlimited repositories - ✅ Custom security rules - Value: Large companies, compliance-driven orgs

Cost Analysis

AI Costs (Per PR with 45 vulnerabilities):

Scenario 1: Suggestions Only (Phase 1)
├── Analysis: $0.05 (existing)
├── Fix Generation: 45 fixes × $0.01 = $0.45
└── Total: $0.50 per PR

Scenario 2: Auto-Fix + Suggestions (Phase 2)
├── Analysis: $0.05
├── Tier 1 Auto-fixes: 5 fixes × $0 = $0 (pattern-based)
├── Tier 2 Suggestions: 40 fixes × $0.01 = $0.40
└── Total: $0.45 per PR

Volume Estimate:
├── 1000 PRs/day = $450/day = $13,500/month in AI costs
├── Revenue (100 teams at $99/mo): $9,900/month
└── Margin: Negative (need volume or higher pricing)

Break-even Analysis:
├── Need ~150 teams at $99/mo to cover AI costs
├── OR 50 Enterprise customers at $299/mo

Cost Optimization Strategies: - Cache common fix patterns (reduce AI calls) - Use smaller/cheaper models for simple fixes - Batch process fixes (fewer API calls) - Offer "fast mode" (pattern-only) vs "AI mode" (paid)

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Risk Mitigation

Technical Risks

Risk 1: AI generates bad fixes

Mitigation:
├── Human review required (Phase 1-2)
├── Syntax validation after fix generation
├── User can modify suggestion before applying
├── "Report Bad Fix" button
└── Track bad fix rate, pause if >5%

Risk 2: Slow performance (5+ min per PR)

Mitigation:
├── Async processing (no timeout issues)
├── Show partial results as they complete
├── Cache common fix patterns
└── Use faster models for simple patterns

Risk 3: High AI costs

Mitigation:
├── Free tier excludes AI suggestions
├── Paid tiers have usage limits
├── Optimize prompts (fewer tokens)
└── Use pattern matching where possible

Legal Risks

Risk 1: Liability for bad AI fixes

Mitigation:
├── T&Cs disclaimer: "AI-generated, review required"
├── Human review in the loop (not auto-applied)
├── Developer explicitly chooses to apply
├── "Generated by AI" badge on all suggestions
└── Insurance coverage for tech E&O

Risk 2: AI training data copyright

Mitigation:
├── Use commercial AI APIs (Together AI, OpenAI)
├── Don't train custom models on user code
├── Clear T&Cs about data usage
└── Opt-out option for sensitive repos

Business Risks

Risk 1: Low adoption (users don't apply suggestions)

Detection:
├── Measure acceptance rate in Phase 1
├── If <70%, suggests feature isn't valuable

Response:
├── Iterate on fix quality
├── Improve UX friction
├── OR pivot to different feature

Risk 2: Competitors copy feature

Mitigation:
├── Speed of execution (ship Phase 1 in 3 weeks)
├── Quality moat (security expertise + good prompts)
├── Network effects (ML improves with usage)
└── Brand trust (establish reputation first)

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Success Metrics Dashboard

Phase 1 Metrics (Nov-Dec 2025)

Product Metrics:

Weekly Tracking:
├── Suggestions Generated: Target 100/week
├── Suggestions Applied: Target >70 (70% acceptance)
├── Bad Fix Reports: Target <5 (5% bad fix rate)
├── Time Saved: Target 2 hours/user/week
└── User NPS: Target >40

Technical Metrics:

Performance:
├── Fix Generation Time: Target <10s per fix
├── API Success Rate: Target >99%
├── Suggestion Quality (AI eval): Target >90%
└── Syntax Validation Pass Rate: Target >95%

Phase 2 Metrics (Dec 2025 - Jan 2026)

Tiered Performance:

Tier 1 (Auto-fix):
├── Fixes Applied: Target 100/week
├── Issues Introduced: Target 0
├── User Complaints: Target 0

Tier 2 (Suggestions):
├── Acceptance by Pattern Type:
│   ├── SQL Injection: >90%
│   ├── XSS: >85%
│   ├── Weak Crypto: >95%
│   └── Path Traversal: >80%

Business Metrics:

Revenue Impact:
├── Paid Plan Signups: Track uplift from feature
├── Churn Rate: Target <5% (feature stickiness)
├── Expansion Revenue: Users upgrade for unlimited fixes
└── Customer Acquisition Cost: Track if feature reduces CAC

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Open Questions & Decisions Needed

Technical Decisions

Q1: Which AI model to use for fix generation?

Options:
A. Together AI (Qwen2.5-Coder-32B) - Current, optimized for code
B. OpenAI GPT-4 - More expensive, better quality?
C. Anthropic Claude - Good reasoning, unknown code performance

Decision: Start with Together AI (A), A/B test vs Claude in Phase 2

Q2: How to handle framework detection?

Options:
A. Regex pattern matching (fast, brittle)
B. AST parsing (slow, accurate)
C. AI inference from code (slow, context-aware)

Decision: Hybrid - patterns for common cases, AI for edge cases

Q3: Should we support custom fix rules?

Enterprise feature idea:
- Company defines security patterns
- AI learns company-specific fixes
- Example: "Always use our internal sanitize() function"

Decision: Not in Phase 1, evaluate for Enterprise tier in Phase 3

Business Decisions

Q1: Pricing for fix suggestions?

Options:
A. Unlimited (simple, risky at scale)
B. Usage-based (10/50/unlimited per month)
C. Seat-based (same as team members)

Decision: Usage-based (B) - 10 fixes/month Free, unlimited Team/Enterprise

Q2: Free tier strategy?

Options:
A. No AI fixes in free (drive paid conversions)
B. 10 fixes/month free (grow user base)
C. Unlimited syntax, paid for security (hybrid)

Decision: Option C - Maximize adoption while protecting margins

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Next Steps (Immediate Actions)

This Week (Nov 14-21)

Day 1: Documentation & Core Implementation ✅ COMPLETE - [x] Create AUTO_FIX_ROADMAP.md - [x] Update Phase 7 docs with strategic pivot - [x] Add User Responsibilities & Risk Acknowledgment section - [x] Add generateSuggestions() method to FixApplier - [x] Integrate Together AI for security fix generation - [x] Create FixSuggestion interface (14 fields) - [x] Write comprehensive tests (19 new tests, 38 total, all passing)

What Was Built (Nov 14):

// New interfaces:
- FixSuggestion (14 fields) - Complete suggestion structure
- GenerateSuggestionsInput - Input parameters
- GenerateSuggestionsResult - Output with summary

// New methods:
- generateSuggestions() - Main entry point (280+ lines)
- generateSingleFixSuggestion() - Per-vulnerability processing
- buildFixPrompt() - AI prompt engineering
- parseFixResponse() - JSON parsing with fallbacks
- generateDiffPreview() - Unified diff generation
- estimateEffort() - Time-to-apply estimation

// Features:
- Multi-language support (JavaScript, Python, Java)
- Framework awareness (Express, Django, Spring)
- Confidence levels (high/medium/low)
- Smart severity filtering
- Context extraction (5 lines before/after)
- Error tracking per vulnerability

Day 2-3: PR Comment Formatter (Next) - [ ] Update comment template with suggestions section - [ ] Add diff preview rendering - [ ] Add "Apply This Fix" button with API endpoint - [ ] Show confidence level badges

Day 4-5: Apply Fix API - [ ] Create /api/teams/[id]/apply-fix endpoint - [ ] Implement Git commit creation - [ ] Add error handling and validation - [ ] Test end-to-end workflow

Next Week (Nov 21-28)

Internal Testing: - [ ] Create 5 test PRs with known vulnerabilities - [ ] Generate suggestions for all 5 PRs - [ ] Apply fixes and verify correctness - [ ] Measure time savings vs manual fix

Beta Recruitment: - [ ] Identify 5-10 beta testers - [ ] Create test repository with vulnerabilities - [ ] Schedule screen-share sessions - [ ] Prepare feedback survey

Week 3 (Nov 28 - Dec 5)

Beta Testing: - [ ] Run 5-10 beta sessions - [ ] Collect acceptance rate data - [ ] Gather qualitative feedback - [ ] Identify UX friction points

Iteration: - [ ] Fix bugs found in testing - [ ] Improve AI prompts for low-quality suggestions - [ ] Polish UI based on feedback - [ ] Prepare Phase 1 completion report

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Appendix: Alternative Approaches Considered

Option A: Full Auto-Fix (Rejected)

What it was: - AI automatically commits security fixes to PR - No human review required - Fastest user experience

Why rejected: - Too risky (AI mistakes could break production) - High liability (company blamed for bad fixes) - Enterprise won't adopt (requires human review for compliance) - Expensive ($13.5k/month AI costs with no validation)

Option B: Detection Only (Rejected)

What it was: - Find vulnerabilities, explain them, stop there - No fix suggestions at all - Safest approach

Why rejected: - Not differentiated (competitors already do this) - Low value-add (users still do manual work) - Doesn't leverage AI strengths - Hard to justify premium pricing

Option C: AI-Suggested Fixes (SELECTED)

What it is: - AI generates fix suggestions - Developer reviews and applies - Progressive automation over time

Why selected: - Best risk/reward balance - Aligns with current product UX - Validates market demand before scaling - Builds competitive moat - Protects brand reputation

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Document History

Date	Version	Changes	Author
Nov 23, 2025	1.2	Added Phase 4: IaC Security Support (Future Roadmap - Q3-Q4 2026)	Claude + Vitor
Nov 14, 2025 17:35	1.1	Phase 1 Week 1 COMPLETE - Added User Responsibilities & Risk Acknowledgment section	Claude + Vitor
Nov 14, 2025 14:00	1.0	Initial roadmap created after strategic decision	Claude + Vitor

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Phase 4: IaC Security Support (Future - Q3-Q4 2026)

Timeline: July - December 2026 (TENTATIVE)

Goal: Extend CodeSlick to Infrastructure as Code (IaC) security scanning and auto-fix.

Market Opportunity

Market Size: \(2B+ cloud security misconfiguration market **Validation Events:** - Bridgecrew (IaC security): Acquired by Palo Alto Networks for **\)156M** (2021) - Snyk IaC: Part of $8.5B valuation company - Checkov: 6.8k GitHub stars (open source IaC scanner)

Strategic Rationale

Why This Makes Sense:

CodeSlick Architecture → IaC Application:
├── Static analysis → Terraform/CloudFormation parsing
├── Pattern matching → Detect misconfigurations
├── Auto-fix PRs → Fix publicly_accessible = true → false
├── CVSS scoring → Map to CIS Benchmarks, NIST
├── GitHub integration → DevOps teams live in PRs
└── Compliance → PCI-DSS, SOC2, ISO 27001

Customer Fit: - Your B2B customers (DevSecOps teams) write BOTH: - Application code (JavaScript, Python, Java) - Infrastructure code (Terraform, CloudFormation, Kubernetes) - One platform for all code security = compelling value proposition

Competitive Advantages: - ✅ Speed: <3s analysis (competitors take 10-30s) - ✅ Auto-fix PRs: Snyk/Checkov only detect, CodeSlick fixes automatically - ✅ Multi-code platform: App code + IaC (competitors do one or the other) - ✅ Existing customer base: Sell to current users (lower CAC)

Why NOT Now (Phase 7-8)

Critical Risks:

❌ Premature - No revenue yet (first customers target: Nov 2025)
❌ Scope creep - Would delay monetization by 6-12 months
❌ Resource drain - Solo/small team needs focus
❌ Market competition - Bridgecrew/Snyk well-established
❌ Technical complexity - 4-6 new language parsers needed

Strategic Principle: Revenue validates product-market fit. IaC is distraction before PMF.

Launch Prerequisites (MUST MEET ALL)

Do NOT start IaC work unless:

✅ Revenue: €10k+ MRR (Monthly Recurring Revenue)
✅ Customer Validation: 10+ users explicitly request IaC support
✅ Core Product: Phases 1-3 complete and stable
✅ Team: Revenue funds IaC specialist hire OR solo founder has bandwidth
✅ Market Research: Competitive landscape still has opening

MVP Scope (Minimal Viable IaC - 2-3 months)

Phase 4.1: Terraform Only (Q3 2026)

Supported Format: - ✅ Terraform (.tf files only) - ❌ CloudFormation (not yet) - ❌ Kubernetes YAML (not yet) - ❌ Ansible (not yet)

10 Critical Security Checks:

1. Hardcoded secrets (already have this!)
   ❌ password = "admin123"
   ✅ password = var.db_password

2. Public S3 buckets
   ❌ acl = "public-read"
   ✅ acl = "private"

3. Unencrypted storage
   ❌ encrypted = false
   ✅ encrypted = true

4. Open security groups
   ❌ cidr_blocks = ["0.0.0.0/0"] # Port 22
   ✅ cidr_blocks = ["10.0.0.0/8"]

5. Missing logging
   ❌ logging { enabled = false }
   ✅ logging { enabled = true }

6. Weak IAM permissions
   ❌ "Action": "*"
   ✅ "Action": ["s3:GetObject"]

7. Public RDS instances
   ❌ publicly_accessible = true
   ✅ publicly_accessible = false

8. Unencrypted RDS
   ❌ storage_encrypted = false
   ✅ storage_encrypted = true

9. Missing backups
   ❌ backup_retention_period = 0
   ✅ backup_retention_period = 7

10. Insecure TLS
    ❌ ssl_enforcement_enabled = false
    ✅ ssl_enforcement_enabled = true

Compliance Mapping: - CIS AWS Foundations Benchmark - NIST 800-53 (basic) - PCI-DSS (storage/network)

Effort Estimate: 2-3 months full-time

Positioning: "Bonus feature" not core offering - Marketing: "Now with Terraform scanning!" - Pricing: Keep same (€99-299/month, no price increase) - Don't compete with Bridgecrew/Snyk directly yet

Phase 4.2: Full IaC Platform (Q4 2026+)

IF Terraform MVP proves popular (>50% users adopt):

Expand Formats: - CloudFormation (AWS) - Kubernetes YAML - Ansible playbooks - Pulumi (Python/TypeScript)

Advanced Features: - 50+ IaC security checks - State file analysis (drift detection) - Cost optimization suggestions - Compliance frameworks (full CIS, SOC2, ISO 27001) - Custom rules engine

Pricing: - Enterprise tier: "Complete Code Security Platform" = €499/month - IaC-only tier: €199/month (for infra teams)

Competition: - Now compete directly with Snyk IaC, Bridgecrew/Prisma Cloud - Differentiation: Speed + auto-fix + multi-code platform

Decision Criteria & Tracking

Track Customer Demand (Starting Now):

PostHog Event: iac_feature_request
Sales Questions: "What other code do you need scanned?"
Support Tickets: Tag IaC-related requests
Survey: "Would you pay 50% more for Terraform scanning?"

Launch Triggers:

Option A: High Organic Demand
├── 10+ customers explicitly request IaC
├── Willing to pay €50-100/month extra
└── Decision: Start Phase 4.1 (Terraform MVP)

Option B: Strategic Expansion
├── Hit €20k+ MRR on core product
├── 5+ Enterprise customers ask for IaC
├── Hire IaC security specialist
└── Decision: Build full platform (Phase 4.2)

Option C: Market Opportunity
├── Competitor (Checkov/Bridgecrew) acquired/shut down
├── Market gap opens up
└── Decision: Fast-follow with basic support

Abandon Triggers:

❌ 0-2 customers request IaC after 6 months
❌ Core product revenue <€5k MRR (need focus)
❌ Terraform landscape becomes too competitive
→ Decision: Stay focused on app code security

Competitive Landscape (2026)

Established Players: | Company | Strength | Weakness | CodeSlick Advantage | |---------|----------|----------|---------------------| | Bridgecrew/Prisma Cloud | Enterprise, comprehensive | Expensive, slow | Speed, pricing, auto-fix | | Snyk IaC | Brand, integrations | Detection only, costly | Auto-fix PRs, simplicity | | Terraform Cloud | Native integration | Basic checks | Deep security focus | | Checkov | Free, open source | No auto-fix, no support | Commercial support, UX |

Market Positioning:

If we launch IaC:
"CodeSlick: Complete Code Security Platform"
├── Application Code: JS, Python, Java, TS
├── Infrastructure Code: Terraform, CloudFormation, K8s
├── One Platform: Unified dashboard, pricing, workflow
└── Auto-Fix Everything: PRs for both app + infra issues

Success Metrics (IF Launched)

Phase 4.1 (Terraform MVP - 3 months):

Product Metrics:
├── Terraform repos scanned: >50
├── IaC issues detected: >500
├── Fixes applied: >100
├── User adoption: >40% of customers try IaC
└── Retention: >80% continue using after trial

Business Metrics:
├── Revenue impact: Track upsells from IaC
├── CAC: Lower (sell to existing customers)
├── NPS: >50 for IaC feature
└── Churn: <5% (adds stickiness)

Phase 4.2 (Full Platform - 6 months):

Market Position:
├── IaC checks: 50+ (vs Checkov's 100+)
├── Speed: <5s (vs competitors' 15-30s)
├── Auto-fix rate: >60% issues
└── Compliance: 3+ frameworks (CIS, NIST, PCI)

Financial:
├── IaC revenue: €5k+ MRR
├── Enterprise tier: 10+ customers at €499/mo
└── Total ARR: €150k+ (€10k app code + €5k IaC)

Risk Mitigation

Technical Risks:

Risk: Terraform parsing too complex
Mitigation: Use HashiCorp's HCL parser library

Risk: False positives on valid patterns
Mitigation: Conservative rules, user feedback loop

Risk: Performance issues on large .tf files
Mitigation: Parallel parsing, caching

Business Risks:

Risk: Low adoption (<10% of users)
Response: Don't expand beyond Terraform MVP

Risk: Competitors copy auto-fix feature
Response: Speed + quality moat, keep iterating

Risk: Drains focus from core product
Response: Strict prerequisites (€10k MRR minimum)

Summary: Strategic Recommendation

Aspect	Rating	Assessment
Market Opportunity	⭐⭐⭐⭐⭐	MASSIVE ($2B+ market, proven exits)
Technical Fit	⭐⭐⭐⭐⭐	PERFECT (auto-fix architecture ideal)
Customer Fit	⭐⭐⭐⭐⭐	SAME buyers (DevSecOps teams)
Timing	⭐	TOO EARLY (no revenue yet)
Strategic Priority	Phase 10+	After €10k MRR + customer validation

Final Verdict: - 🎯 Document now (this roadmap) - 📊 Track demand (PostHog, surveys, sales) - 🚫 Don't build (until prerequisites met) - ✅ Revisit (Q2 2026 after revenue validation)

This is a $100M+ opportunity... for 2027. Stay focused on Phase 7-8 for 2025-2026.

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References

• Phase 7 Overview: docs/phases/PHASE_4_WEEK_5_DAY_4_COMPLETE.md
• Current Implementation: src/lib/github/fix-applier.ts
• Competitive Analysis: docs/business/FOUNDER_CHEAT_SHEET.md
• Security Coverage: docs/technical/ANALYZER_COVERAGE.md