Test-Driven Problem Solving in Coding Interviews¶
Coding interviews can be intimidating, but there's a systematic approach that can boost your confidence and improve your success rate: Test-Driven Development (TDD). Today, we'll explore how to apply TDD principles to coding interview problems for clearer thinking and better solutions.
Why TDD for Interviews?¶
Traditional interview advice often focuses on algorithms and data structures, but how you approach problems matters just as much. TDD provides:
- Clarity of thought through concrete examples
- Incremental progress that builds confidence
- Automatic edge case discovery
- Built-in verification of your solution
- Clear communication with your interviewer
The TDD Interview Framework¶
1. Understand & Clarify (Red Phase)¶
Before writing any code, create failing tests that capture your understanding:
def test_two_sum_basic():
# Test basic functionality
assert two_sum([2, 7, 11, 15], 9) == [0, 1]
def test_two_sum_edge_cases():
# Test edge cases you discover through questions
assert two_sum([3, 3], 6) == [0, 1] # Duplicates
assert two_sum([1, 2], 4) == None # No solution
Key Questions to Ask: - What should happen with duplicates? - What if no solution exists? - Are negative numbers allowed? - Can I use the same element twice?
2. Implement Incrementally (Green Phase)¶
Start with the simplest solution that passes your tests:
def two_sum(nums, target):
"""Find indices of two numbers that add up to target"""
# Brute force - O(n²) but correct
for i in range(len(nums)):
for j in range(i + 1, len(nums)):
if nums[i] + nums[j] == target:
return [i, j]
return None
3. Optimize & Refactor (Refactor Phase)¶
Now improve your solution while keeping tests green:
def two_sum(nums, target):
"""Find indices of two numbers that add up to target - O(n) solution"""
seen = {}
for i, num in enumerate(nums):
complement = target - num
if complement in seen:
return [seen[complement], i]
seen[num] = i
return None
Real Interview Example: Valid Parentheses¶
Let's walk through a complete example using the "Valid Parentheses" problem.
Step 1: Write Tests First¶
def test_valid_parentheses():
# Basic cases
assert is_valid("()") == True
assert is_valid("()[]{}") == True
assert is_valid("([{}])") == True
# Invalid cases
assert is_valid("(]") == False
assert is_valid("([)]") == False
# Edge cases
assert is_valid("") == True # Empty string
assert is_valid("(") == False # Unmatched opening
assert is_valid(")") == False # Unmatched closing
assert is_valid("((") == False # Multiple unmatched
Step 2: Implement Solution¶
def is_valid(s):
"""Check if string has valid parentheses"""
stack = []
mapping = {')': '(', '}': '{', ']': '['}
for char in s:
if char in mapping: # Closing bracket
if not stack or stack.pop() != mapping[char]:
return False
else: # Opening bracket
stack.append(char)
return len(stack) == 0
Step 3: Verify and Optimize¶
Run all tests to ensure correctness, then consider optimizations:
def is_valid_optimized(s):
"""Optimized version with early termination"""
if len(s) % 2 != 0: # Odd length can't be valid
return False
stack = []
pairs = {'(': ')', '[': ']', '{': '}'}
for char in s:
if char in pairs: # Opening bracket
stack.append(pairs[char])
elif not stack or stack.pop() != char: # Closing bracket
return False
return len(stack) == 0
Advanced TDD Techniques for Interviews¶
1. Property-Based Testing Mindset¶
Think about invariants and properties:
def test_binary_search_properties():
arr = [1, 3, 5, 7, 9, 11]
# Property: If element exists, index should be correct
for i, val in enumerate(arr):
assert binary_search(arr, val) == i
# Property: If element doesn't exist, should return -1
assert binary_search(arr, 0) == -1
assert binary_search(arr, 12) == -1
assert binary_search(arr, 4) == -1
2. Boundary Testing¶
Always test the edges:
def test_merge_intervals_boundaries():
# Single interval
assert merge([[1, 4]]) == [[1, 4]]
# No overlap
assert merge([[1, 2], [3, 4]]) == [[1, 2], [3, 4]]
# Complete overlap
assert merge([[1, 4], [2, 3]]) == [[1, 4]]
# Adjacent intervals
assert merge([[1, 3], [3, 5]]) == [[1, 5]]
# Empty input
assert merge([]) == []
3. Performance Testing¶
Consider time/space complexity in your tests:
def test_fibonacci_performance():
import time
# Test that optimized version is fast enough
start = time.time()
result = fibonacci_optimized(40)
duration = time.time() - start
assert result == 102334155
assert duration < 0.001 # Should be nearly instant
Communication Strategy¶
During the Interview¶
-
Share your test cases: "Let me start by writing some test cases to make sure I understand the problem"
-
Explain your thinking: "This test case covers the edge case where..."
-
Show incremental progress: "Great, my basic solution passes all tests. Now let me optimize..."
-
Validate as you go: "Let me trace through this test case to verify my logic..."
Example Dialog¶
You: "Let me write a few test cases first to make sure I understand the problem correctly."
Interviewer: "Good approach, go ahead."
You: "For the two-sum problem, I'll test the basic case [2,7,11,15] with target 9, expecting indices [0,1]. I should also test duplicates like [3,3] with target 6. What should happen if there's no solution?"
Interviewer: "Return null or empty array."
You: "Perfect, I'll add that test case too. Now I'll implement a solution that passes these tests..."
Common Pitfalls to Avoid¶
❌ Don't Skip the Test Phase¶
# Bad: Jump straight to implementation
def two_sum(nums, target):
# Hope this works...
for i in range(len(nums)):
# ...
✅ Start with Tests¶
# Good: Define expected behavior first
def test_two_sum():
assert two_sum([2, 7, 11, 15], 9) == [0, 1]
def two_sum(nums, target):
# Now implement to satisfy the test
❌ Don't Ignore Edge Cases¶
✅ Test Comprehensively¶
# Good: Cover edge cases
def test_reverse_string():
assert reverse_string("hello") == "olleh" # Basic
assert reverse_string("") == "" # Empty
assert reverse_string("a") == "a" # Single char
assert reverse_string("ab") == "ba" # Two chars
Practice Problems to Try¶
Start applying TDD to these classic problems:
- Two Sum - Hash map optimization
- Valid Parentheses - Stack-based solution
- Merge Intervals - Sorting and merging
- Binary Search - Divide and conquer
- Longest Substring Without Repeating Characters - Sliding window
For each problem: 1. Write comprehensive test cases first 2. Implement brute force solution 3. Optimize while keeping tests green 4. Add performance considerations
Conclusion¶
Test-Driven Development isn't just for production code—it's a powerful tool for coding interviews that:
- Clarifies requirements through concrete examples
- Reduces bugs through continuous validation
- Demonstrates systematic thinking to interviewers
- Builds confidence through incremental progress
- Improves communication by making assumptions explicit
Remember: The goal isn't just to solve the problem—it's to demonstrate how you think, communicate, and approach complex challenges systematically.
Next Steps¶
- Practice TDD on LeetCode problems
- Time yourself writing tests vs. implementation
- Record yourself explaining your approach
- Get feedback from peers on your communication style
Ready to level up your interview game? Start with one problem today and apply the TDD approach. You'll be surprised how much clearer your thinking becomes!
Questions about TDD in interviews? Want to share your experience? Reach out on LinkedIn or Twitter!