System Design (LLD)Code Coverage and Test Quality

Code Coverage and Test Quality

LevelIntermediate

Duration60 mins

TopicCode Coverage and Test Quality

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Coverage Types: Line, Branch, Path

Beyond Simple Percentage

When a coverage tool reports "85% coverage," what exactly does that 85% represent? The answer depends on the type of coverage being measured. Different coverage types analyze different aspects of code execution, each revealing distinct insights about test completeness.

Understanding coverage types is like understanding different medical tests. A blood pressure reading, cholesterol panel, and cardiac stress test all measure "heart health," but they examine it from different angles and catch different problems. Similarly, line coverage, branch coverage, and path coverage all measure "test completeness" but detect different gaps in your test suite.

This page explores the three most important coverage types, building from the simplest to the most comprehensive—and most demanding.

What You Will Learn

By the end of this page, you will understand line coverage, statement coverage, branch coverage, and path coverage. You'll know what each type measures, how they relate to one another, their respective strengths and limitations, and how to choose the right coverage type for different testing contexts.

Line and Statement Coverage

Line coverage (also called statement coverage) is the simplest and most commonly reported coverage metric. It measures the percentage of executable lines (or statements) in your code that were executed during testing.

The calculation:

Line Coverage = (Lines Executed / Total Executable Lines) × 100%

Note that "executable lines" excludes blank lines, comments, import statements, and declarations. Only lines that produce runtime behavior count.

Line Coverage Example
Example Code
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function calculateShipping(weight: number, destination: string): number {
  let baseCost = 5.00;                    // Line 2: Executable
  
  if (weight > 10) {                      // Line 4: Executable
    baseCost += (weight - 10) * 0.50;     // Line 5: Executable
  }
  
  if (destination === 'international') {  // Line 8: Executable
    baseCost *= 2.5;                      // Line 9: Executable
  }
  
  return Math.max(baseCost, 3.00);        // Line 12: Executable
}
 
// Test: calculateShipping(5, 'domestic')
// Executes: Lines 2, 4, 8, 12
// Misses: Lines 5, 9
// Line Coverage: 4/6 = 66.7%

What line coverage reveals:

Which functions and methods have been invoked
Whether the "happy path" through the code has been tested
Obvious gaps where entire functions or blocks are never executed

What line coverage misses:

Line coverage has a critical blind spot: it doesn't distinguish how a line was reached. Consider a line inside a conditional:

if (a || b) {
  doSomething(); // This line is "covered" if either a OR b is true
}

If your test only triggers this line when a is true, you get 100% line coverage for this block—but you've never tested the scenario where a is false and b is true. The behavior might differ between these cases, and line coverage won't catch the gap.

Line Coverage is Necessary but Insufficient

Line coverage serves as a good starting point—if a line is never executed, it's definitely not tested. But high line coverage can mask significant gaps. A test suite with 90% line coverage might still miss critical edge cases hidden in conditional logic.

Line Coverage Characteristics
Aspect	Description
Measures	Percentage of executable lines that ran during tests
Granularity	Coarse—whole lines, regardless of internal logic
Ease of Achievement	Relatively easy to get high percentages
Common Threshold	70-90% for most production code
Best For	Quick health check, identifying obviously untested code
Limitation	Ignores conditional complexity within lines

Branch Coverage (Decision Coverage)

Branch coverage (also called decision coverage) goes deeper than line coverage. It measures whether every possible branch from each decision point has been executed. For every if, else, switch case, ternary operator, and loop condition, branch coverage asks: Have we tested both the "taken" and "not taken" paths?

The calculation:

Branch Coverage = (Branches Executed / Total Branches) × 100%

A simple if statement creates two branches (true and false). A switch with five cases creates five branches. A for loop creates two branches (condition true to continue, condition false to exit).

Branch Coverage Analysis
Code with Branches
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function getDiscountTier(
    purchaseCount: number, 
    memberYears: number
): string {
  // Decision 1: Two branches (true/false)
  if (purchaseCount >= 100) {
    // Decision 2: Two branches (true/false)
    if (memberYears >= 5) {
      return 'platinum';           // Branch 1A, 2A
    } else {
      return 'gold';               // Branch 1A, 2B
    }
  }
  
  // Decision 3: Two branches (true/false)
  if (purchaseCount >= 50) {
    return 'silver';               // Branch 1B, 3A
  }
  
  return 'bronze';                 // Branch 1B, 3B
}
 
// Total branches: 6 (2 from Decision 1, 2 from Decision 2, 2 from Decision 3)
 
// Test 1: getDiscountTier(150, 10) → 'platinum'
//   Covers: Decision 1(true), Decision 2(true)
// Test 2: getDiscountTier(25, 1) → 'bronze'
//   Covers: Decision 1(false), Decision 3(false)
// Branch Coverage: 4/6 = 66.7%
 
// Missing: Decision 2(false), Decision 3(true)
// Need: getDiscountTier(150, 2) → 'gold'
//       getDiscountTier(75, 1) → 'silver'

Why branch coverage matters:

Branch coverage catches edge cases that line coverage misses. Consider error handling:

function fetchUser(id: string): User {
  const response = await api.get(`/users/${id}`);
  if (!response.ok) {
    throw new UserNotFoundError(id);
  }
  return response.data;
}

With line coverage, you might achieve 100% by only testing successful fetches—the throw line counts as "covered" even if it never actually throws. Branch coverage requires you to test the !response.ok case explicitly.

Branch Coverage Strengths

•Forces testing of conditional logic
•Ensures error paths are exercised
•Reveals "defensive code" that's never triggered
•More meaningful than line coverage for complex code
•Closer correlation to actual behavioral testing

Branch Coverage Limitations

•Harder to achieve high percentages
•Doesn't test all combinations of branches
•Compound conditions still hide complexity
•Loop boundary conditions may be missed
•Doesn't verify correctness, only execution

Branch Coverage is Often the Sweet Spot

For most production code, branch coverage provides the best balance of insight vs. effort. It catches significantly more issues than line coverage without the exponential test explosion of path coverage. Industry standard targets are often 70-80% branch coverage.

Condition Coverage

Condition coverage examines the individual boolean sub-expressions within a decision. While branch coverage treats if (a && b) as a single decision with two outcomes, condition coverage drills into the components: Has a been both true and false? Has b been both true and false?

The calculation:

Condition Coverage = (Boolean Conditions Evaluated to True + False / Total Possible Evaluations) × 100%

For a condition with two sub-expressions, there are 4 possible evaluations (each can be true or false), but condition coverage only requires that each sub-expression is true at least once and false at least once.

Condition vs. Branch Coverage
Compound Condition
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function isEligibleForLoan(
    creditScore: number, 
    income: number, 
    hasCollateral: boolean
): boolean {
  // Three conditions: A && B && C
  // A: creditScore >= 700
  // B: income >= 50000
  // C: hasCollateral
  
  if (creditScore >= 700 && income >= 50000 && hasCollateral) {
    return true;
  }
  return false;
}
 
// BRANCH COVERAGE requires:
// - At least one test where entire condition is TRUE
// - At least one test where entire condition is FALSE
// Minimum: 2 tests
 
// CONDITION COVERAGE requires each sub-condition evaluated both ways:
// - creditScore >= 700: tested with ≥700 AND <700
// - income >= 50000: tested with ≥50000 AND <50000
// - hasCollateral: tested with true AND false
 
// Test 1: isEligibleForLoan(750, 60000, true) → true
//   A=true, B=true, C=true
// Test 2: isEligibleForLoan(650, 40000, false) → false
//   A=false, B=<not evaluated due to short-circuit>, C=<not evaluated>
 
// Problem: Short-circuit evaluation means B and C might not be tested!

The short-circuit problem:

In most programming languages, compound boolean expressions use short-circuit evaluation:

A && B: If A is false, B is never evaluated
A || B: If A is true, B is never evaluated

This means achieving true condition coverage requires careful test design to ensure every sub-expression actually executes with both true and false values—even when short-circuiting could skip them.

Modified Condition/Decision Coverage (MC/DC):

For safety-critical systems (avionics, medical devices, automotive), a stricter variant called MC/DC is often required. MC/DC demands that each condition independently affects the decision outcome. This means proving that flipping any single condition—while holding others constant—changes the final result.

MC/DC is mathematically rigorous but requires significantly more tests.

When Condition Coverage Matters

Condition coverage becomes important when compound boolean expressions encode complex business rules. If your eligibility logic combines multiple criteria with AND/OR, testing each criterion's influence independently catches bugs that branch coverage misses.

Path Coverage

Path coverage is the most comprehensive (and demanding) coverage type. It measures whether every possible execution path through the code has been tested. A path is a unique sequence of branches taken from function entry to function exit.

The calculation:

Path Coverage = (Unique Paths Executed / Total Possible Paths) × 100%

The exponential problem:

Paths explode combinatorially. Consider a function with 10 sequential if statements:

Each if creates 2 possible branches
Total paths = 2^10 = 1,024 unique paths

Add a loop, and paths become potentially infinite (the loop could execute 0, 1, 2, ... n times). In practice, full path coverage is often infeasible for non-trivial code.

Path Explosion Example
Simple Function with Many Paths
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function processOrder(order: Order): ProcessResult {
  let total = order.subtotal;
  
  // Decision 1: 2 paths
  if (order.hasCoupon) {
    total -= applyCoupon(order.couponCode);
  }
  
  // Decision 2: 2 paths → now 2 × 2 = 4 cumulative paths
  if (order.isMember) {
    total *= 0.95; // 5% member discount
  }
  
  // Decision 3: 2 paths → now 4 × 2 = 8 cumulative paths
  if (total > 100) {
    total -= 10; // $10 off orders over $100
  }
  
  // Decision 4: 2 paths → now 8 × 2 = 16 cumulative paths
  if (order.isRush) {
    total += getRushFee(order.destination);
  }
  
  // Decision 5: 3 paths (express/standard/economy) → 16 × 3 = 48 paths!
  switch (order.shippingTier) {
    case 'express': total += 20; break;
    case 'standard': total += 10; break;
    case 'economy': total += 5; break;
  }
  
  return { total, status: 'processed' };
}
 
// This simple function has 48 unique execution paths!
// 100% path coverage requires 48 distinct test cases.

When path coverage is valuable:

Despite its impracticality for full coverage, path coverage thinking is valuable for:

Critical algorithms — Security, cryptography, and financial calculations where every path must be verified
State machines — Where paths through states represent user workflows
Compiler/interpreter hot paths — Performance-critical code where all code paths matter
Regulatory requirements — Some safety standards mandate path coverage for specific components

Practical path coverage strategies:

Rather than pursuing 100% path coverage (often impossible), experienced engineers:

Identify the most common paths and ensure those are covered
Test boundary paths (shortest, longest, error paths)
Use risk analysis to prioritize high-impact paths
Apply path coverage to small, critical functions while using branch coverage elsewhere

Coverage Type Comparison
Coverage Type	What It Measures	Test Count Growth	Typical Target
Line/Statement	Lines executed	Linear	80-90%
Branch/Decision	Decision outcomes taken	Linear to # of branches	70-85%
Condition	Boolean sub-expressions	Linear to # of conditions	70-80%
Path	Complete execution traces	Exponential (2^n)	Selective, <100%

Path Coverage is Theoretically Complete but Practically Limited

100% path coverage would theoretically test every possible execution scenario. But in practice, it's computationally intractable for most real-world code. Use path coverage thinking to identify critical paths, but rely on branch coverage for systematic coverage targets.

Function and Class Coverage

While line, branch, and path coverage examine code at a granular level, function coverage and class coverage operate at a higher level of abstraction. They answer simpler questions: Was this function ever called? Was this class ever instantiated?

Function coverage:

Function Coverage = (Functions Called / Total Functions) × 100%

Function coverage is coarse—it doesn't care what happens inside the function, only whether the function was invoked. This is useful for quickly identifying dead code or forgotten utilities.

Function Coverage Use Cases

•Quickly identifying which modules have any test coverage at all
•Finding completely untested utility functions
•Detecting dead code (functions that are never called in production OR tests)
•High-level health check before diving into detailed coverage
•API surface coverage (are all public methods tested?)

Class Coverage Considerations

•Ensures every class can be instantiated (constructor works)
•Identifies abstract classes that may lack direct tests
•Highlights DTOs/models that might only need basic coverage
•Catches namespace-level dead code
•Useful for large codebases with many modules

Why function coverage alone is insufficient:

A function like calculateTax(income, deductions, filingStatus) might have dozens of internal branches handling different tax brackets, deduction types, and filing scenarios. Calling it once achieves 100% function coverage while potentially exercising less than 10% of its logic.

Function coverage answers: "Is this function tested at all?" Line/branch coverage answers: "How thoroughly is this function tested?"

Both questions matter, but they reveal different insights.

Function vs. Line Coverage Example
Example
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class TaxCalculator {
  calculate(income: number, status: 'single' | 'married'): number {
    let tax = 0;
    
    if (status === 'married') {
      // Married tax brackets
      if (income <= 20000) tax = income * 0.10;
      else if (income <= 80000) tax = 2000 + (income - 20000) * 0.15;
      else if (income <= 160000) tax = 11000 + (income - 80000) * 0.25;
      else tax = 31000 + (income - 160000) * 0.30;
    } else {
      // Single tax brackets  
      if (income <= 10000) tax = income * 0.10;
      else if (income <= 40000) tax = 1000 + (income - 10000) * 0.15;
      else if (income <= 80000) tax = 5500 + (income - 40000) * 0.25;
      else tax = 15500 + (income - 80000) * 0.30;
    }
    
    return Math.round(tax * 100) / 100;
  }
}
 
// Test: calculator.calculate(50000, 'single')
// Function coverage: 100% (calculate was called)
// Line coverage: ~25% (only one bracket for one status)
// Branch coverage: ~15% (1 of 8 bracket conditions + unmarried path)

Choosing the Right Coverage Type

Different situations call for different coverage emphases. There's no universal "best" coverage type—the right choice depends on context, risk, and resource constraints.

Decision framework:

Coverage Type Selection Guide
Situation	Recommended Coverage	Rationale
Quick health check	Function + Line	Fast to measure, reveals obvious gaps
General production code	Line + Branch	Good balance of insight and effort
Complex business logic	Branch + Condition	Ensures all decision combinations tested
Safety-critical code	MC/DC + selective Path	Regulatory requirements, maximum rigor
API surface validation	Function (public methods)	Ensures all public contracts have tests
Legacy code assessment	Line	Baseline understanding before improving
Performance hotspots	Path (selective)	All execution variants optimized

Layered coverage strategy:

Sophisticated teams often apply different coverage standards to different parts of the codebase:

Tier 1: Critical/Payments/Security — 90%+ branch coverage, condition coverage for compound logic
Tier 2: Core Business Logic — 80%+ line coverage, 70%+ branch coverage
Tier 3: Utilities and Infrastructure — 70%+ line coverage
Tier 4: Generated Code/DTOs — Basic instantiation tests only

This tiered approach allocates testing effort proportionally to risk rather than uniformly across all code.

Start with Branch Coverage

For most teams, branch coverage provides the best default. It catches significantly more bugs than line coverage without the exponential explosion of path coverage. Treat branch coverage as your primary metric, with line coverage as a quick sanity check.

Coverage Type Hierarchies

Coverage types form a hierarchy of strength. Stronger coverage types subsume weaker ones—achieving a stronger coverage level automatically achieves the weaker levels.

The subsumption hierarchy:

Path Coverage
    ↓ (subsumes)
Branch Coverage / Decision Coverage
    ↓ (subsumes)
Statement Coverage / Line Coverage
    ↓ (subsumes)
Function Coverage

What subsumption means:

100% path coverage guarantees 100% branch, line, and function coverage
100% branch coverage guarantees 100% line coverage
100% line coverage guarantees 100% function coverage (for functions with executable statements)

However, the reverse is not true:

100% line coverage does NOT guarantee 100% branch coverage
100% branch coverage does NOT guarantee 100% path coverage

Subsumption Demonstration
Example
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function sign(n: number): string {
  if (n > 0) {
    return "positive";
  }
  if (n < 0) {
    return "negative";
  }
  return "zero";
}
 
// Test Set A: sign(5), sign(-3)
// - Line Coverage: 100% (all lines executed)
// - Branch Coverage: 100% (both branches of both ifs tested)
// - Function Coverage: 100% (function called)
// But wait—we never tested the "zero" case!
// Actually we did: sign(5) tests n>0=true, n<0=not-reached
//                  sign(-3) tests n>0=false, n<0=true
// Where's zero? If we add sign(0):
// - n>0=false, n<0=false, reaches "zero" return
 
// Test Set B: sign(5), sign(-3), sign(0)
// Now we have complete branch coverage: 
// - n>0: true(5), false(-3,0)
// - n<0: true(-3), false(0)
// Line coverage: 100%
// Branch coverage: 100%
// Path coverage: 100% (only 3 paths exist, all tested)

Practical implication:

If you track branch coverage, you don't need to separately track line coverage—achieving branch coverage targets will naturally achieve line coverage targets (assuming reasonable test design). But tracking both can be useful for different audiences:

Executives might see line coverage (simpler to understand)
Engineers should focus on branch coverage (more meaningful)
Architects may examine path coverage for critical components

Summary: Coverage Types

We've explored the landscape of coverage types, from simple line coverage to comprehensive path coverage. Let's consolidate the key insights:

Key Takeaways

•Line coverage measures executed statements—simple but misses conditional complexity.
•Branch coverage measures decision outcomes—the sweet spot for most production code.
•Condition coverage examines individual boolean sub-expressions—important for compound logic.
•Path coverage measures all execution traces—comprehensive but exponentially expensive.
•Function coverage is coarse but useful for identifying completely untested code.
•Coverage types form a hierarchy—stronger types subsume weaker ones.
•Choose coverage type based on risk—critical code warrants stricter coverage than utilities.
•Branch coverage is the recommended default—it balances insight with practicality.

What's next:

Now that we understand different coverage types, we need to confront an uncomfortable truth: even high coverage by any measure has significant limitations. The next page explores what coverage cannot tell you and why relying solely on coverage metrics leads teams astray.

Page Complete

You now understand the spectrum of coverage types—from simple line coverage through demanding path coverage. You know what each measures, their trade-offs, and how to select the right type for different contexts. Next, we'll explore the critical limitations of coverage metrics.

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Loading learning content...

System Design (LLD)Code Coverage and Test Quality

Code Coverage and Test Quality

LevelIntermediate

Duration60 mins

TopicCode Coverage and Test Quality

2 / 4

Coverage Types: Line, Branch, Path

Beyond Simple Percentage

This page explores the three most important coverage types, building from the simplest to the most comprehensive—and most demanding.

What You Will Learn

Line and Statement Coverage

The calculation:

Line Coverage = (Lines Executed / Total Executable Lines) × 100%

Note that "executable lines" excludes blank lines, comments, import statements, and declarations. Only lines that produce runtime behavior count.

Line Coverage Example
Example Code
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function calculateShipping(weight: number, destination: string): number {
  let baseCost = 5.00;                    // Line 2: Executable
  
  if (weight > 10) {                      // Line 4: Executable
    baseCost += (weight - 10) * 0.50;     // Line 5: Executable
  }
  
  if (destination === 'international') {  // Line 8: Executable
    baseCost *= 2.5;                      // Line 9: Executable
  }
  
  return Math.max(baseCost, 3.00);        // Line 12: Executable
}
 
// Test: calculateShipping(5, 'domestic')
// Executes: Lines 2, 4, 8, 12
// Misses: Lines 5, 9
// Line Coverage: 4/6 = 66.7%

What line coverage reveals:

Which functions and methods have been invoked
Whether the "happy path" through the code has been tested
Obvious gaps where entire functions or blocks are never executed

What line coverage misses:

Line coverage has a critical blind spot: it doesn't distinguish how a line was reached. Consider a line inside a conditional:

if (a || b) {
  doSomething(); // This line is "covered" if either a OR b is true
}

Line Coverage is Necessary but Insufficient

Line Coverage Characteristics
Aspect	Description
Measures	Percentage of executable lines that ran during tests
Granularity	Coarse—whole lines, regardless of internal logic
Ease of Achievement	Relatively easy to get high percentages
Common Threshold	70-90% for most production code
Best For	Quick health check, identifying obviously untested code
Limitation	Ignores conditional complexity within lines

Branch Coverage (Decision Coverage)

The calculation:

Branch Coverage = (Branches Executed / Total Branches) × 100%

Branch Coverage Analysis
Code with Branches
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function getDiscountTier(
    purchaseCount: number, 
    memberYears: number
): string {
  // Decision 1: Two branches (true/false)
  if (purchaseCount >= 100) {
    // Decision 2: Two branches (true/false)
    if (memberYears >= 5) {
      return 'platinum';           // Branch 1A, 2A
    } else {
      return 'gold';               // Branch 1A, 2B
    }
  }
  
  // Decision 3: Two branches (true/false)
  if (purchaseCount >= 50) {
    return 'silver';               // Branch 1B, 3A
  }
  
  return 'bronze';                 // Branch 1B, 3B
}
 
// Total branches: 6 (2 from Decision 1, 2 from Decision 2, 2 from Decision 3)
 
// Test 1: getDiscountTier(150, 10) → 'platinum'
//   Covers: Decision 1(true), Decision 2(true)
// Test 2: getDiscountTier(25, 1) → 'bronze'
//   Covers: Decision 1(false), Decision 3(false)
// Branch Coverage: 4/6 = 66.7%
 
// Missing: Decision 2(false), Decision 3(true)
// Need: getDiscountTier(150, 2) → 'gold'
//       getDiscountTier(75, 1) → 'silver'

Why branch coverage matters:

Branch coverage catches edge cases that line coverage misses. Consider error handling:

function fetchUser(id: string): User {
  const response = await api.get(`/users/${id}`);
  if (!response.ok) {
    throw new UserNotFoundError(id);
  }
  return response.data;
}

Branch Coverage Strengths

•Forces testing of conditional logic
•Ensures error paths are exercised
•Reveals "defensive code" that's never triggered
•More meaningful than line coverage for complex code
•Closer correlation to actual behavioral testing

Branch Coverage Limitations

•Harder to achieve high percentages
•Doesn't test all combinations of branches
•Compound conditions still hide complexity
•Loop boundary conditions may be missed
•Doesn't verify correctness, only execution

Branch Coverage is Often the Sweet Spot

Condition Coverage

The calculation:

Condition Coverage = (Boolean Conditions Evaluated to True + False / Total Possible Evaluations) × 100%

Condition vs. Branch Coverage
Compound Condition
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function isEligibleForLoan(
    creditScore: number, 
    income: number, 
    hasCollateral: boolean
): boolean {
  // Three conditions: A && B && C
  // A: creditScore >= 700
  // B: income >= 50000
  // C: hasCollateral
  
  if (creditScore >= 700 && income >= 50000 && hasCollateral) {
    return true;
  }
  return false;
}
 
// BRANCH COVERAGE requires:
// - At least one test where entire condition is TRUE
// - At least one test where entire condition is FALSE
// Minimum: 2 tests
 
// CONDITION COVERAGE requires each sub-condition evaluated both ways:
// - creditScore >= 700: tested with ≥700 AND <700
// - income >= 50000: tested with ≥50000 AND <50000
// - hasCollateral: tested with true AND false
 
// Test 1: isEligibleForLoan(750, 60000, true) → true
//   A=true, B=true, C=true
// Test 2: isEligibleForLoan(650, 40000, false) → false
//   A=false, B=<not evaluated due to short-circuit>, C=<not evaluated>
 
// Problem: Short-circuit evaluation means B and C might not be tested!

The short-circuit problem:

In most programming languages, compound boolean expressions use short-circuit evaluation:

A && B: If A is false, B is never evaluated
A || B: If A is true, B is never evaluated

Modified Condition/Decision Coverage (MC/DC):

MC/DC is mathematically rigorous but requires significantly more tests.

When Condition Coverage Matters

Path Coverage

The calculation:

Path Coverage = (Unique Paths Executed / Total Possible Paths) × 100%

The exponential problem:

Paths explode combinatorially. Consider a function with 10 sequential if statements:

Each if creates 2 possible branches
Total paths = 2^10 = 1,024 unique paths

Add a loop, and paths become potentially infinite (the loop could execute 0, 1, 2, ... n times). In practice, full path coverage is often infeasible for non-trivial code.

Path Explosion Example
Simple Function with Many Paths
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function processOrder(order: Order): ProcessResult {
  let total = order.subtotal;
  
  // Decision 1: 2 paths
  if (order.hasCoupon) {
    total -= applyCoupon(order.couponCode);
  }
  
  // Decision 2: 2 paths → now 2 × 2 = 4 cumulative paths
  if (order.isMember) {
    total *= 0.95; // 5% member discount
  }
  
  // Decision 3: 2 paths → now 4 × 2 = 8 cumulative paths
  if (total > 100) {
    total -= 10; // $10 off orders over $100
  }
  
  // Decision 4: 2 paths → now 8 × 2 = 16 cumulative paths
  if (order.isRush) {
    total += getRushFee(order.destination);
  }
  
  // Decision 5: 3 paths (express/standard/economy) → 16 × 3 = 48 paths!
  switch (order.shippingTier) {
    case 'express': total += 20; break;
    case 'standard': total += 10; break;
    case 'economy': total += 5; break;
  }
  
  return { total, status: 'processed' };
}
 
// This simple function has 48 unique execution paths!
// 100% path coverage requires 48 distinct test cases.

When path coverage is valuable:

Despite its impracticality for full coverage, path coverage thinking is valuable for:

Critical algorithms — Security, cryptography, and financial calculations where every path must be verified
State machines — Where paths through states represent user workflows
Compiler/interpreter hot paths — Performance-critical code where all code paths matter
Regulatory requirements — Some safety standards mandate path coverage for specific components

Practical path coverage strategies:

Rather than pursuing 100% path coverage (often impossible), experienced engineers:

Identify the most common paths and ensure those are covered
Test boundary paths (shortest, longest, error paths)
Use risk analysis to prioritize high-impact paths
Apply path coverage to small, critical functions while using branch coverage elsewhere

Coverage Type Comparison
Coverage Type	What It Measures	Test Count Growth	Typical Target
Line/Statement	Lines executed	Linear	80-90%
Branch/Decision	Decision outcomes taken	Linear to # of branches	70-85%
Condition	Boolean sub-expressions	Linear to # of conditions	70-80%
Path	Complete execution traces	Exponential (2^n)	Selective, <100%

Path Coverage is Theoretically Complete but Practically Limited

Function and Class Coverage

Function coverage:

Function Coverage = (Functions Called / Total Functions) × 100%

Function coverage is coarse—it doesn't care what happens inside the function, only whether the function was invoked. This is useful for quickly identifying dead code or forgotten utilities.

Function Coverage Use Cases

•Quickly identifying which modules have any test coverage at all
•Finding completely untested utility functions
•Detecting dead code (functions that are never called in production OR tests)
•High-level health check before diving into detailed coverage
•API surface coverage (are all public methods tested?)

Class Coverage Considerations

•Ensures every class can be instantiated (constructor works)
•Identifies abstract classes that may lack direct tests
•Highlights DTOs/models that might only need basic coverage
•Catches namespace-level dead code
•Useful for large codebases with many modules

Why function coverage alone is insufficient:

Function coverage answers: "Is this function tested at all?" Line/branch coverage answers: "How thoroughly is this function tested?"

Both questions matter, but they reveal different insights.

Function vs. Line Coverage Example
Example
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class TaxCalculator {
  calculate(income: number, status: 'single' | 'married'): number {
    let tax = 0;
    
    if (status === 'married') {
      // Married tax brackets
      if (income <= 20000) tax = income * 0.10;
      else if (income <= 80000) tax = 2000 + (income - 20000) * 0.15;
      else if (income <= 160000) tax = 11000 + (income - 80000) * 0.25;
      else tax = 31000 + (income - 160000) * 0.30;
    } else {
      // Single tax brackets  
      if (income <= 10000) tax = income * 0.10;
      else if (income <= 40000) tax = 1000 + (income - 10000) * 0.15;
      else if (income <= 80000) tax = 5500 + (income - 40000) * 0.25;
      else tax = 15500 + (income - 80000) * 0.30;
    }
    
    return Math.round(tax * 100) / 100;
  }
}
 
// Test: calculator.calculate(50000, 'single')
// Function coverage: 100% (calculate was called)
// Line coverage: ~25% (only one bracket for one status)
// Branch coverage: ~15% (1 of 8 bracket conditions + unmarried path)

Choosing the Right Coverage Type

Different situations call for different coverage emphases. There's no universal "best" coverage type—the right choice depends on context, risk, and resource constraints.

Decision framework:

Coverage Type Selection Guide
Situation	Recommended Coverage	Rationale
Quick health check	Function + Line	Fast to measure, reveals obvious gaps
General production code	Line + Branch	Good balance of insight and effort
Complex business logic	Branch + Condition	Ensures all decision combinations tested
Safety-critical code	MC/DC + selective Path	Regulatory requirements, maximum rigor
API surface validation	Function (public methods)	Ensures all public contracts have tests
Legacy code assessment	Line	Baseline understanding before improving
Performance hotspots	Path (selective)	All execution variants optimized

Layered coverage strategy:

Sophisticated teams often apply different coverage standards to different parts of the codebase:

Tier 1: Critical/Payments/Security — 90%+ branch coverage, condition coverage for compound logic
Tier 2: Core Business Logic — 80%+ line coverage, 70%+ branch coverage
Tier 3: Utilities and Infrastructure — 70%+ line coverage
Tier 4: Generated Code/DTOs — Basic instantiation tests only

This tiered approach allocates testing effort proportionally to risk rather than uniformly across all code.

Start with Branch Coverage

Coverage Type Hierarchies

Coverage types form a hierarchy of strength. Stronger coverage types subsume weaker ones—achieving a stronger coverage level automatically achieves the weaker levels.

The subsumption hierarchy:

Path Coverage
    ↓ (subsumes)
Branch Coverage / Decision Coverage
    ↓ (subsumes)
Statement Coverage / Line Coverage
    ↓ (subsumes)
Function Coverage

What subsumption means:

100% path coverage guarantees 100% branch, line, and function coverage
100% branch coverage guarantees 100% line coverage
100% line coverage guarantees 100% function coverage (for functions with executable statements)

However, the reverse is not true:

100% line coverage does NOT guarantee 100% branch coverage
100% branch coverage does NOT guarantee 100% path coverage

Subsumption Demonstration
Example
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function sign(n: number): string {
  if (n > 0) {
    return "positive";
  }
  if (n < 0) {
    return "negative";
  }
  return "zero";
}
 
// Test Set A: sign(5), sign(-3)
// - Line Coverage: 100% (all lines executed)
// - Branch Coverage: 100% (both branches of both ifs tested)
// - Function Coverage: 100% (function called)
// But wait—we never tested the "zero" case!
// Actually we did: sign(5) tests n>0=true, n<0=not-reached
//                  sign(-3) tests n>0=false, n<0=true
// Where's zero? If we add sign(0):
// - n>0=false, n<0=false, reaches "zero" return
 
// Test Set B: sign(5), sign(-3), sign(0)
// Now we have complete branch coverage: 
// - n>0: true(5), false(-3,0)
// - n<0: true(-3), false(0)
// Line coverage: 100%
// Branch coverage: 100%
// Path coverage: 100% (only 3 paths exist, all tested)

Practical implication:

Executives might see line coverage (simpler to understand)
Engineers should focus on branch coverage (more meaningful)
Architects may examine path coverage for critical components

Summary: Coverage Types

We've explored the landscape of coverage types, from simple line coverage to comprehensive path coverage. Let's consolidate the key insights:

Key Takeaways

•Line coverage measures executed statements—simple but misses conditional complexity.
•Branch coverage measures decision outcomes—the sweet spot for most production code.
•Condition coverage examines individual boolean sub-expressions—important for compound logic.
•Path coverage measures all execution traces—comprehensive but exponentially expensive.
•Function coverage is coarse but useful for identifying completely untested code.
•Coverage types form a hierarchy—stronger types subsume weaker ones.
•Choose coverage type based on risk—critical code warrants stricter coverage than utilities.
•Branch coverage is the recommended default—it balances insight with practicality.

What's next:

Page Complete

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