Case Studies

Explore real-world applications of CodeLogician across different domains and industries. Each case study demonstrates how formal verification solves complex problems in production systems with mathematical certainty.

Why These Case Studies Matter

These case studies showcase how CodeLogician helps teams:

• Detect redundant rules that add unnecessary complexity
• Verify critical properties with mathematical certainty
• Discover hidden bugs that traditional testing misses
• Validate regulatory compliance against published specifications
• Analyze all possible behaviors through region decomposition
• Quantify complexity growth from feature additions

Each case study includes real code, the verification process, concrete results, and downloadable examples.

Financial Services

Algorithmic Trading Rules Analysis

Domain: Financial Services - Trading Algorithms
Problem: Complex trading rules with potential overlaps and redundancies
Solution: Formal verification to detect unnecessary rules and prove correctness

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Key Results:

• Identified multiple redundant rules in 300+ rule system
• Proved critical trading constraints hold for all inputs
• Reduced complexity through formal analysis
• Verification Time: Minutes to analyze complex rule interactions

What you'll learn:

• How to identify redundant business rules
• Performing region decomposition for exhaustive behavior analysis
• Verifying critical trading constraints with formal proofs
• Using formal methods to simplify complex rule systems

LSE GTT Order Expiry Verification

Domain: Financial Trading Systems - Order Management
Problem: Critical regulatory compliance bug in auction order expiry logic
Solution: Formal verification discovers race condition that traditional testing missed

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Key Results:

• Critical Bug Found: GTT orders expiring during auction uncross
• MIT201 Violation: Orders incorrectly excluded from auctions
• Detection Time: Under 1 minute with concrete counterexample
• Impact: Prevented potential regulatory violations and customer losses

What you'll learn:

• How to find critical bugs through formal verification
• Discovering feature interaction bugs in complex systems
• Verifying regulatory compliance requirements (MIT201)
• Using counterexamples to understand and fix bugs

LSE Stop Orders Verification

Domain: Financial Trading Systems - Risk Management
Problem: Extended functionality bypassing MIT201 specification rules
Solution: Formal verification validates core implementation and discovers bug in custom extensions

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Key Results:

• Core Implementation Validated: MIT201 rules correctly implemented
• Extension Bug Found: Large order logic bypasses safety checks
• Time Saved: ~8 hours of manual review
• Confidence: Mathematical proof of bug existence with executable counterexample

What you'll learn:

• Validating compliance with published specifications (MIT201)
• Detecting bugs in extended functionality that bypasses core rules
• Using verification goals to test safety properties
• Understanding how "temporary hacks" can violate critical invariants

Interest Rate Swap Schedule Generator

Domain: Financial Services - Derivative Instruments
Problem: Business day adjustment logic in swap scheduling with subtle edge cases
Solution: Formal verification ensures mathematical correctness and discovers edge cases

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Key Results:

• Property Proved: No weekend payments (Saturday/Sunday) mathematically guaranteed
• Edge Case Found: Date collision for consecutive weekend dates (documented)
• Monotonicity Proved: For all swap frequencies (monthly+), dates strictly increase
• Market Scale: $400+ trillion notional outstanding globally

What you'll learn:

• Verifying business day adjustment logic with formal proofs
• Using region decomposition to identify all behavioral paths
• Discovering edge cases that traditional testing would miss
• Providing mathematical proofs for regulatory compliance (EMIR, Dodd-Frank, ISDA)

Bank Account Classification Analysis

Domain: Financial Services - Deposit Accounts
Problem: Simple feature addition creates exponential complexity growth
Solution: Region decomposition reveals 2.9x increase in testing requirements

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Key Results:

• Complexity Growth: 19 regions → 55 regions (2.9x increase)
• Hidden Edge Cases: Formal analysis discovered 36 new behavioral regions
• Testing Impact: 190% increase in test cases needed
• Insight: 21% more code created 190% more complexity

What you'll learn:

• How seemingly simple changes create hidden complexity
• Why traditional testing misses edge cases
• Using region decomposition for complete test coverage
• Quantifying the impact of feature additions on testing effort

Margin Account Engine Verification

Domain: Financial Services - Prime Brokerage / Risk Management
Problem: Regulatory margin enforcement in leveraged trading accounts
Solution: Formal verification ensures solvency protection and compliance

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Key Results:

• Solvency Safety: Low equity MUST trigger liquidation ✅ PROVED
• No Gap Risk: Permitted withdrawals cannot cause margin calls ✅ PROVED
• Withdrawal Protection: Every withdrawal maintains initial margin ✅ PROVED
• Risk Eliminated: Archegos-style failures provably impossible

What you'll learn:

• Verifying state machine transitions with formal proofs
• Ensuring regulatory compliance (Regulation T, FINRA 4210)
• Using Decimal arithmetic for exact financial calculations
• Region decomposition for complete test coverage

LSE Equity Fees Verification

Domain: Financial Services - Exchange Fee Calculations
Problem: Complex fee schedule with tiered pricing, rebates, and minimum floors
Solution: Formal verification proves invariants and discovers 6 behavioral regions

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Key Results:

• Invariant Proved: "Penny Jump" — IOC/FOK costs exactly £0.01 more than DAY
• Invariant Proved: LPS Zero-Fee — 0.00bp rate with 0.00 min = exactly zero
• Hidden Complexity: 6 behavioral regions discovered (expected 2-3)
• Audit-Ready: Every calculation traces to PDF source reference

What you'll learn:

• Verifying exchange fee calculations with mathematical proofs
• Discovering hidden minimum charge boundaries through decomposition
• Using formal verification for MiFID II compliance evidence
• Enabling O(1) fee lookups via pre-computed region tables

Financial Infrastructure

Multilateral Netting Engine Verification

Domain: Financial Infrastructure - Central Counterparties
Problem: Floating-point precision and validation bugs in settlement calculations
Solution: Formal verification discovers critical bugs before any test code written

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Key Results:

• Bug #1 Found: Input validation gap allowing negative trade amounts
• Bug #2 Found: Floating-point precision failure ($1,400 daily discrepancy)
• Fixed: Migrated to Decimal arithmetic with defense-in-depth validation
• Detection Time: < 5 minutes to find both production-breaking bugs

What you'll learn:

• Why floating-point arithmetic is dangerous in financial systems
• Implementing defense-in-depth validation strategies
• Using verification goals as executable regulatory requirements
• Ensuring EMIR/Dodd-Frank compliance with mathematical proofs

Case Study Overview

Industry Applications

These case studies demonstrate CodeLogician's applicability across:

Getting Started

Coming Soon

More case studies across different domains are being added:

• Healthcare Systems - Eligibility determination and dosage calculations
• Aerospace Software - Flight control system verification
• Automotive Controls - Safety-critical embedded systems
• Blockchain Smart Contracts - Contract correctness and security properties
• State Machine Verification - Protocol state management
• Access Control Systems - Authorization and permission rule verification

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