interval-guided-regression-test-update

name

interval-guided-regression-test-update

description

Automatically updates regression tests based on interval analysis to maintain coverage of key program intervals. Use when code changes affect value ranges, conditionals, or control flow, and existing tests need updating to maintain interval coverage. Analyzes interval information from updated code, identifies coverage gaps, adjusts test inputs and assertions, removes redundant tests, and generates new tests for uncovered intervals. Supports Python, Java, JavaScript, and C/C++ with various test frameworks (pytest, JUnit, Jest, Google Test).

Interval-Guided Regression Test Update

Automatically update regression tests to maintain interval coverage when program code changes.

Core Concept

Intervals represent ranges of values that variables can take during execution. When code changes, intervals may be added, removed, or modified. This skill ensures regression tests continue to cover all important intervals.

Example:

# Old code
def process(x):
    if x < 10:
        return x * 2
    else:
        return x + 10

# Intervals: [0, 10), [10, ∞)

# New code (added negative handling)
def process(x):
    if x < 0:
        return -x
    elif x < 10:
        return x * 2
    else:
        return x + 10

# Intervals: (-∞, 0), [0, 10), [10, ∞)
# Need to add test for new interval: x < 0

Workflow

1. Analyze Existing Tests

Parse the current regression test suite:

• Identify test inputs
• Determine which intervals each test covers
• Calculate current interval coverage

# Example test analysis
test_process_small: input=5, covers [0, 10)
test_process_large: input=15, covers [10, ∞)
# Coverage: 2/2 intervals = 100%

2. Extract Interval Information

Obtain interval information from the updated program:

From static analysis:

• Parse conditionals (if, switch, etc.)
• Extract comparison operators
• Derive interval constraints

From profiling:

• Run existing tests
• Track observed value ranges
• Identify intervals exercised

From symbolic execution:

• Execute with symbolic values
• Collect path constraints
• Derive interval constraints

See references/interval-analysis.md for detailed extraction methods.

3. Identify Coverage Gaps

Compare current coverage with new intervals:

old_intervals = {[0, 10), [10, ∞)}
new_intervals = {(-∞, 0), [0, 10), [10, ∞)}

added_intervals = {(-∞, 0)}  # Need new test
removed_intervals = {}
modified_intervals = {}

4. Update Tests

Apply appropriate update strategies:

Add tests for new intervals:

# New test for (-∞, 0)
def test_process_negative():
    assert process(-5) == 5

Adjust inputs for modified intervals:

# If boundary changed from x < 10 to x < 20
# Old: test_process_large with input=15
# New: test_process_large with input=25

Update assertions for changed behavior:

# If logic changed
# Old: assert process(5) == 10
# New: assert process(5) == 15

Remove redundant tests:

# If intervals merged, remove duplicate coverage

See references/test-update-strategies.md for detailed strategies.

5. Validate Updated Tests

Run the updated test suite:

• Execute all tests
• Verify all pass
• Check interval coverage
• Identify any remaining gaps

6. Generate Coverage Report

Provide comprehensive summary:

• Before/after interval coverage
• Tests added, modified, removed
• Validation results
• Remaining gaps (if any)

Quick Start Examples

Example 1: New Interval Added

Scenario: Function adds handling for negative numbers

Original code:

def abs_double(x):
    if x < 10:
        return x * 2
    else:
        return x + 10

Original tests:

def test_small():
    assert abs_double(5) == 10

def test_large():
    assert abs_double(15) == 25

Updated code:

def abs_double(x):
    if x < 0:
        return -x * 2
    elif x < 10:
        return x * 2
    else:
        return x + 10

Updated tests:

def test_negative():  # NEW
    assert abs_double(-5) == 10

def test_small():
    assert abs_double(5) == 10

def test_large():
    assert abs_double(15) == 25

Summary:

• Added interval: (-∞, 0)
• Added test: test_negative
• Coverage: 2/2 → 3/3 (100%)

Example 2: Interval Boundary Changed

Scenario: Threshold value modified

Original code:

def categorize(score):
    if score < 60:
        return "fail"
    else:
        return "pass"

Original tests:

def test_fail():
    assert categorize(50) == "fail"

def test_pass():
    assert categorize(70) == "pass"

Updated code:

def categorize(score):
    if score < 50:  # Changed from 60
        return "fail"
    else:
        return "pass"

Updated tests:

def test_fail():
    assert categorize(40) == "fail"  # Changed input from 50

def test_pass():
    assert categorize(60) == "pass"  # Changed input from 70

Summary:

• Modified interval: [0, 60) → [0, 50)
• Modified interval: [60, ∞) → [50, ∞)
• Updated 2 test inputs
• Coverage: 2/2 → 2/2 (100%)

Example 3: Intervals Merged

Scenario: Simplified logic combines cases

Original code:

def classify(x):
    if x < 0:
        return "negative"
    elif x == 0:
        return "zero"
    else:
        return "positive"

Original tests:

def test_negative():
    assert classify(-5) == "negative"

def test_zero():
    assert classify(0) == "zero"

def test_positive():
    assert classify(5) == "positive"

Updated code:

def classify(x):
    if x <= 0:
        return "non-positive"
    else:
        return "positive"

Updated tests:

def test_non_positive():  # Merged
    assert classify(-5) == "non-positive"
    assert classify(0) == "non-positive"

def test_positive():
    assert classify(5) == "positive"

Summary:

• Merged intervals: (-∞, 0) and {0} → (-∞, 0]
• Removed test: test_zero (redundant)
• Updated test: test_negative → test_non_positive
• Coverage: 3/3 → 2/2 (100%)

Update Strategies

Strategy 1: Input Adjustment

Modify test inputs to cover new or changed intervals.

When to use:

• New interval added
• Interval boundary changed
• Need to cover previously uncovered interval

How:

1. Identify target interval
2. Select representative value from interval
3. Update test input
4. Verify test still valid

Strategy 2: Assertion Update

Modify expected values when behavior changes.

When to use:

• Logic changed within interval
• Return value modified
• Side effects changed

How:

1. Execute test with new code
2. Observe actual output
3. Verify output is correct
4. Update assertion

Strategy 3: Test Removal

Remove redundant or obsolete tests.

When to use:

• Intervals merged
• Multiple tests cover same interval
• Interval no longer exists

How:

1. Identify redundant tests
2. Keep most representative test
3. Remove others
4. Verify coverage maintained

Strategy 4: Test Generation

Create new tests for uncovered intervals.

When to use:

• New interval added
• Coverage gap identified
• No existing test covers interval

How:

1. Identify uncovered interval
2. Generate representative input
3. Determine expected output
4. Create new test

Coverage Analysis

Computing Coverage

Interval Coverage = (Covered Intervals) / (Total Intervals) × 100%

Example:

Total intervals: 4
Covered by tests: 3
Coverage: 75%

Coverage Report Format

Interval Coverage Report
========================

Before Update:
- Total intervals: 2
- Covered intervals: 2
- Coverage: 100%

After Update:
- Total intervals: 3
- Covered intervals: 3
- Coverage: 100%

Changes:
- Added intervals: 1
- Removed intervals: 0
- Modified intervals: 0

Test Changes:
- Added tests: 1
- Modified tests: 0
- Removed tests: 0

Best Practices

Minimize Changes

• Only update tests affected by code changes
• Preserve tests that still provide value
• Avoid unnecessary rewrites

Maintain Test Quality

• Use descriptive test names
• Keep tests simple and focused
• Document why tests were updated

Validate Thoroughly

• Run all tests after updates
• Check coverage metrics
• Verify no regressions

Document Changes

• Record what changed and why
• Note any manual adjustments
• Track coverage improvements

Common Scenarios

Scenario 1: Refactoring with Logic Change

Problem: Code refactored, some logic changed

Solution:

1. Extract intervals from new code
2. Compare with old intervals
3. Update tests for changed intervals
4. Verify all tests pass

Scenario 2: Feature Addition

Problem: New feature adds new code paths

Solution:

1. Identify new intervals
2. Generate tests for new intervals
3. Ensure existing tests still valid
4. Verify complete coverage

Scenario 3: Bug Fix

Problem: Bug fix changes behavior in specific interval

Solution:

1. Identify affected interval
2. Update test for that interval
3. Add regression test if needed
4. Verify fix doesn't break other intervals

Troubleshooting

Tests Fail After Update

Check:

• Assertions match new behavior
• Inputs are valid for new code
• Expected values are correct

Solution:

• Review code changes carefully
• Execute tests manually
• Update assertions as needed

Coverage Decreased

Check:

• New intervals added
• Tests removed incorrectly
• Intervals not properly identified

Solution:

• Identify uncovered intervals
• Generate missing tests
• Verify interval extraction

Redundant Tests

Check:

• Multiple tests cover same interval
• Intervals merged in new code

Solution:

• Identify redundant tests
• Keep most representative
• Remove duplicates

References

• interval-analysis.md: Comprehensive guide to interval concepts, extraction methods, and coverage analysis
• test-update-strategies.md: Detailed strategies for updating tests including input adjustment, assertion updates, and test generation

Tips

• Start with coverage analysis: Understand current coverage before making changes
• Use interval extraction tools: Leverage static analysis or profiling tools
• Update incrementally: Make one change at a time and validate
• Preserve test intent: Keep the purpose of each test clear
• Document assumptions: Note any assumptions about intervals
• Validate coverage: Always verify coverage after updates