[Do not merge] Testing codecov AI flows #1245
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🔍 Existing Issues For ReviewYour pull request is modifying functions with the following pre-existing issues: 📄 File: billing/views.py
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Codecov ReportAttention: Patch coverage is
✅ All tests successful. No failed tests found.
Additional details and impacted files@@ Coverage Diff @@
## main #1245 +/- ##
==========================================
- Coverage 96.31% 94.75% -1.57%
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Files 492 493 +1
Lines 16844 16931 +87
==========================================
- Hits 16224 16043 -181
- Misses 620 888 +268
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![codecov-ai[bot]](https://avatars.githubusercontent.com/in/797565?s=60&v=4){kind=small}
| # Factorial using iterative approach | ||
| def factorial(n): | ||
| if n < 0: | ||
| raise ValueError("Negative numbers do not have factorials") | ||
| result = 1 | ||
| for i in range(1, n + 1): | ||
| result *= i | ||
| return result |
There was a problem hiding this comment.
The factorial implementation currently uses an iterative approach which could encounter stack overflow for large numbers. Consider adding a maximum input validation to prevent potential resource exhaustion attacks. Also, consider documenting the maximum supported input value in the docstring.
| # Factorial using iterative approach | |
| def factorial(n): | |
| if n < 0: | |
| raise ValueError("Negative numbers do not have factorials") | |
| result = 1 | |
| for i in range(1, n + 1): | |
| result *= i | |
| return result | |
| def factorial(n): | |
| if n < 0: | |
| raise ValueError("Negative numbers do not have factorials") | |
| if n > 900: # Prevent stack overflow/resource exhaustion | |
| raise ValueError("Input too large. Maximum supported value is 900") | |
| result = 1 | |
| for i in range(1, n + 1): | |
| result *= i | |
| return result |
| raise ValueError("At least two data points are required to compute variance") | ||
| return variance(self.data) | ||
|
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| def normalize(self): | ||
| m = self.get_mean() | ||
| try: | ||
| std_dev = math.sqrt(variance(self.data)) | ||
| except Exception: | ||
| raise ValueError("Could not compute standard deviation") |
There was a problem hiding this comment.
The normalize method in DataProcessor is not handling potential division by zero correctly. While there is a check for std_dev == 0, the sqrt operation could still raise a ValueError. Consider adding proper error handling and documentation about expected behavior in edge cases.
| raise ValueError("At least two data points are required to compute variance") | |
| return variance(self.data) | |
| def normalize(self): | |
| m = self.get_mean() | |
| try: | |
| std_dev = math.sqrt(variance(self.data)) | |
| except Exception: | |
| raise ValueError("Could not compute standard deviation") | |
| def normalize(self): | |
| m = self.get_mean() | |
| try: | |
| std_dev = math.sqrt(self.get_variance()) | |
| if std_dev == 0: | |
| raise ValueError("Standard deviation is zero, cannot normalize.") | |
| return [(x - m) / std_dev for x in self.data] | |
| except ValueError as e: | |
| raise ValueError(f"Normalization failed: {str(e)}") |
| return default | ||
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| # Merge two dictionaries recursively | ||
| def merge_dicts(dict1, dict2): | ||
| result = dict1.copy() | ||
| for key, value in dict2.items(): | ||
| if key in result and isinstance(result[key], dict) and isinstance(value, dict): | ||
| result[key] = merge_dicts(result[key], value) |
There was a problem hiding this comment.
The merge_dicts function could potentially cause runtime errors with very deep dictionaries due to recursive calls. Consider adding a depth limit parameter and check to prevent stack overflow in case of maliciously crafted input.
| return default | |
| # Merge two dictionaries recursively | |
| def merge_dicts(dict1, dict2): | |
| result = dict1.copy() | |
| for key, value in dict2.items(): | |
| if key in result and isinstance(result[key], dict) and isinstance(value, dict): | |
| result[key] = merge_dicts(result[key], value) | |
| def merge_dicts(dict1, dict2, max_depth=10, current_depth=0): | |
| if current_depth >= max_depth: | |
| raise ValueError(f"Maximum merge depth of {max_depth} exceeded") | |
| result = dict1.copy() | |
| for key, value in dict2.items(): | |
| if key in result and isinstance(result[key], dict) and isinstance(value, dict): | |
| result[key] = merge_dicts(result[key], value, max_depth, current_depth + 1) | |
| else: | |
| result[key] = value | |
| return result |
| ) | ||
|
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| def invoice_payment_failed(self, invoice: stripe.Invoice) -> None: | ||
| """ | ||
| Stripe invoice.payment_failed webhook event is emitted when an invoice payment fails | ||
| (initial or recurring). Note that delayed payment methods (including ACH with | ||
| microdeposits) may have a failed initial invoice until the account is verified. | ||
| """ | ||
| if invoice.default_payment_method is None: |
There was a problem hiding this comment.
The deletion of extensive documentation in the webhook handlers significantly reduces code maintainability. Even if the functionality is being moved or refactored, it's important to maintain clear documentation about the webhook purposes and behaviors. Consider retaining or updating the docstrings.
| ) | |
| def invoice_payment_failed(self, invoice: stripe.Invoice) -> None: | |
| """ | |
| Stripe invoice.payment_failed webhook event is emitted when an invoice payment fails | |
| (initial or recurring). Note that delayed payment methods (including ACH with | |
| microdeposits) may have a failed initial invoice until the account is verified. | |
| """ | |
| if invoice.default_payment_method is None: | |
| def invoice_payment_failed(self, invoice: stripe.Invoice) -> None: | |
| """Handles Stripe invoice.payment_failed webhook events. | |
| Called when an invoice payment fails (initial or recurring). Note that delayed | |
| payment methods (including ACH with microdeposits) may have a failed initial | |
| invoice until the account is verified. | |
| Args: | |
| invoice: The Stripe invoice object containing payment details | |
| """ |
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