Logging Review (Swift)

Review Swift code for improper logging levels and sensitive data in logs.

Parameters

Name	Label	Required	Default
code	Code to Review	Yes	-
context	Context	No	-

Prompt Template

You are a senior code reviewer. Review the following Swift Vapor code for improper logging levels and sensitive data in logs. Code: ```Swift {{code}} ``` Context: {{context}} Instructions: 1. Identify all instances of improper logging levels and sensitive data in logs. 2. For each finding, assign severity: critical / high / medium / low. 3. Provide a concrete code fix for each finding. 4. Note any false-positive risks so the reviewer can verify. 5. Summarize the overall quality at the end. Output a structured review: findings, severity, fix, and summary.

Example

// Reviewing a Swift Vapor handler for improper logging levels and sensitive data in logs // Input: a function that concatenates user input into a query // Finding 1 (critical): unsanitized user input leads to improper logging levels and sensitive data in logs // Fix: use parameterized queries / prepared statements // Summary: 1 critical finding, fix before merge

Tips

✦Focus only on improper logging levels and sensitive data in logs — do not review unrelated issues in this pass.
✦Provide a concrete code fix for every finding, not just a description.
✦Use consistent severity ratings so the team can prioritize effectively.

Why

According to the GitHub 2024 developer survey, 97% of developers use AI coding tools and AI-assisted review catches improper logging levels and sensitive data in logs 55% faster than manual review. Logging Review catches improper logging levels and sensitive data in logs before they reach production, reducing incidents and technical debt. Stack Overflow 2024 reports 76% of developers plan to use AI for code quality, making automated review a baseline expectation.

When

Before merging code that may introduce improper logging levels and sensitive data in logs, or during periodic security/quality audits. Do not use this prompt for trivial style-only changes or when the code has no security/quality surface area.

Common Problems

⚠Error: reviewers drift to style nitpicks — "Use camelCase" instead of "SQL injection on line 42". Fix: enforce a focus scope in the review prompt.
⚠Error: findings lack concrete fixes — "This is vulnerable" with no remediation code. Fix: require a code fix for every finding.
⚠Error: severity ratings inconsistent — same issue rated "low" by one reviewer and "critical" by another. Fix: use a rubric with explicit criteria.

Best Practices

✓Use a fixed checklist so every review covers the same ground every time.
✓Provide a code fix for each finding — a description without a fix is half a review.
✓Track findings over time to identify recurring patterns and training needs.

FAQ

What is this prompt for and why use it?

Review Swift code for improper logging levels and sensitive data in logs. According to the GitHub 2024 developer survey, 97% of developers use AI coding tools and AI-assisted review catches improper logging levels and sensitive data in logs 55% faster than manual review. Logging Review catches improper logging levels and sensitive d

When should I use this prompt?

How do I get the best results with this prompt?

Key tips: Focus only on improper logging levels and sensitive data in logs — do not review unrelated issues in this pass.; Provide a concrete code fix for every finding, not just a description.; Use consistent severity ratings so the team can prioritize effectively..

What common problems should I watch for?

Common problems: Error: reviewers drift to style nitpicks — "Use camelCase" instead of "SQL injection on line 42". Fix: enforce a focus scope in the review prompt.; Error: findings lack concrete fixes — "This is vulnerable" with no remediation code. Fix: require a code fix for every finding.; Error: severity ratings inconsistent — same issue rated "low" by one reviewer and "critical" by another. Fix: use a rubric with explicit criteria..

What are the best practices?

Recommended: Use a fixed checklist so every review covers the same ground every time.; Provide a code fix for each finding — a description without a fix is half a review.; Track findings over time to identify recurring patterns and training needs..