Test: Medium/Low severity findings only

[matej]

Summary

Test PR to verify that inline comments are posted even when the review is APPROVED (no HIGH severity findings).

This file contains intentional issues:

• O(n²) algorithm (MEDIUM - performance)
• Magic numbers (LOW - maintainability)
• Missing empty input handling (LOW - correctness)
• Unused variable (LOW - maintainability)

Expected behavior

• Individual inline comments for each finding
• Final review: APPROVE (not REQUEST_CHANGES)

🤖 Generated with Claude Code

[github-actions]

Summary: 2 findings across 1 file (HIGH: 0, MEDIUM: 2, LOW: 0).
Assessment: Changes look acceptable overall; consider addressing the findings.

[github-actions]

🤖 Code Review Finding: Division by zero when processing empty data

Severity: MEDIUM
Category: correctness

Impact: Any call to process_data([]) will crash the application with a ZeroDivisionError. This is a runtime bug that will cause unexpected failures.

Recommendation: Add a guard clause to handle empty input gracefully, either by returning a default value or raising a meaningful exception.

Suggested change

	def process_data(data):
	"""Process data and return result.
	Low severity: Doesn't handle empty input gracefully.
	"""
	total = 0
	for item in data:
	total += item["value"] # Will crash if data is empty or item has no "value"
	def process_data(data):
	"""Process data and return result.
	Low severity: Doesn't handle empty input gracefully.
	"""
	if not data:
	return 0
	total = 0
	for item in data:
	total += item["value"] # Will crash if data is empty or item has no "value"
	return total / len(data)

[github-actions]

🤖 Code Review Finding: Quadratic time complexity in duplicate detection

Severity: MEDIUM
Category: performance

Impact: For large input lists, this function will become extremely slow. A list of 10,000 items could require up to 100 million comparisons, causing performance degradation in production.

Recommendation: Use a set-based approach to achieve O(n) time complexity.

Suggested change

	def find_duplicates(items):
	"""Find duplicate items in a list.
	Medium severity: O(n²) algorithm when O(n) with set is possible.
	"""
	duplicates = []
	for i in range(len(items)):
	for j in range(i + 1, len(items)):
	if items[i] == items[j] and items[i] not in duplicates:
	duplicates.append(items[i])
	return duplicates
	def find_duplicates(items):
	"""Find duplicate items in a list.
	Medium severity: O(n²) algorithm when O(n) with set is possible.
	"""
	seen = set()
	duplicates = set()
	for item in items:
	if item in seen:
	duplicates.add(item)
	else:
	seen.add(item)
	return list(duplicates)