Automated Lint Issue Detection & Resolution Guide

In modern software development, maintaining code quality is paramount. Automated lint issue detection plays a crucial role in identifying and addressing code quality issues early in the development lifecycle. This article will guide you through the process of automating lint issue detection, from parsing build logs to creating GitHub issues and validating resolutions. We will cover best practices, templates, and audit trails to ensure a smooth and efficient workflow. Let's dive in and explore how to enhance your code quality with automated linting!

Duties Discussion Category: BorDevTech, ClearView

The primary duty of this automated process is to execute immediately following the completion of the "Deploy Next.js site to Pages" workflow. This ensures that any linting or compilation errors are promptly identified and addressed. The core responsibilities include parsing build logs, creating or updating GitHub issues, validating resolutions, maintaining an audit trail, and conducting post-run reviews. Each of these steps is critical in ensuring that code quality standards are upheld throughout the development lifecycle.

1. Parse the Build Logs from the Deploy Run

The initial step in automated lint issue detection involves parsing the build logs generated during the deployment process. This process is critical for identifying any linting and compilation errors that may have occurred. It's important to extract only the relevant errors, while ignoring warnings, telemetry data, and informational messages that do not directly impact code correctness. The goal is to focus on issues that could potentially lead to bugs or performance problems in the application. The precision in this step helps to streamline the subsequent steps and ensures that developers are addressing the most critical issues first.

Error Extraction Details

Each extracted error must contain specific details to facilitate efficient debugging and resolution. These details include the file path, line number, column number, rule name, and the error message itself. This granular information allows developers to pinpoint the exact location of the error within the codebase and understand the nature of the issue. For example, knowing the line and column number makes it easier to navigate to the problem area in the code editor, while the rule name provides context on the specific coding standard or best practice that was violated. The error message offers a concise description of the problem, aiding in the immediate understanding of what needs to be fixed.

Regular Expression for Error Detection

To effectively extract errors from the build logs, a regular expression (regex) pattern is employed. The following regex pattern is used to detect errors:

./app/api/verify/([\w-]+)/([\w-]+\.ts):(\d+):(\d+) Error: (.*?) @([\w-]+)

This regex pattern is designed to capture the necessary details from the log entries. Let's break down the components of this pattern:

• Group 1: ([\w-]+): Captures the folder name (e.g., missouri). The \w represents word characters (letters, numbers, and underscore), and - allows for hyphens in the folder name. The + ensures that one or more such characters are matched.
• Group 2: ([\w-]+\.ts): Captures the file name (e.g., logic.ts). Similar to the folder name, it matches word characters and hyphens, but it also ensures that the file name ends with the .ts extension.
• Group 3: (\d+): Captures the line number. The \d represents a digit, and + ensures that one or more digits are matched.
• Group 4: (\d+): Captures the column number, using the same pattern as the line number.
• Group 5: (.*?): Captures the error message. The . matches any character (except newline), * matches the previous element zero or more times, and ? makes the match non-greedy, ensuring it captures only up to the next specific delimiter.
• Group 6: ([\w-]+): Captures the ESLint rule that was violated.

Normalizing Errors into Structured Objects

Once the errors are extracted using the regex pattern, they are normalized into structured JSON objects. This normalization process is crucial for consistent data handling and facilitates the subsequent steps, such as creating GitHub issues. The structured object format is as follows:

{
  "file": "<folder>/<file>.ts",
  "line": <line>,
  "column": <column>,
  "rule": "<rule>",
  "message": "<message>"
}

Each field in the object corresponds to a specific piece of information about the error:

• file: The relative file path where the error occurred, constructed by combining the folder name and file name (e.g., missouri/logic.ts).
• line: The line number where the error is located.
• column: The column number indicating the precise location of the error.
• rule: The name of the ESLint rule that was violated (e.g., no-unused-vars).
• message: The detailed error message describing the issue.

By structuring the errors in this format, it becomes easier to programmatically process and manage them. This structured data is used to create informative and consistent GitHub issues, allowing developers to quickly understand and address the identified problems.

2. Create or Update GitHub Issues Based on Errors

After parsing the build logs and extracting the errors, the next critical step is to create or update GitHub issues. This process ensures that each identified error is tracked and addressed systematically. The automated creation and updating of issues streamline the workflow, making it easier for developers to manage and resolve code quality problems. The goal is to translate the errors detected in the logs into actionable tasks within the GitHub repository.

Issue Title and Uniqueness

The title of each GitHub issue must correspond to the relative file path where the error occurred. For example, an error in missouri/logic.ts will result in an issue titled missouri/logic.ts. This naming convention ensures that each issue is uniquely identifiable and directly linked to the specific file containing the error. It also prevents the creation of duplicate issues for the same file, which could lead to confusion and inefficiency. Always ensure that no duplicate issues are created for the same file.

Standardized Issue Body Template

The body of each issue must adhere to a standardized template to ensure consistency and clarity. This template provides a structured way to present the error details, suggested fixes, audit trail, and resolution notes. The standardized format helps developers quickly grasp the context of the issue and understand the steps needed to resolve it. Here is the standardized issue body template:

## 📂 File
`<folder>/<file>.ts`

---

## ❌ Current Errors
(Each error listed with a red marker if new/unread, or plain if already tracked.)

- 🔴 **<rule>** at line <line>: <message>
- 🔴 **<rule>** at line <line>: <message>

---

## 🛠 Suggested Fixes
For each error, provide a clear, actionable solution:

- **<rule>** → <suggested fix, e.g. remove unused variable, replace `any` with inferred type, import missing module>
- **<rule>** → <fix explanation with link to ESLint docs>

---

## 📑 Audit Trail
- First detected: `<timestamp>` (Run ID: `<run-id>`)
- Last updated: `<timestamp>` (Run ID: `<run-id>`)
- Status: `Unread` | `In Progress` | `Resolved`

---

## ✅ Resolution Notes
(Only added when error is fixed)

- ✔ <rule> at line <line> resolved in commit `<sha>`
- Signed off by @copilot

The template includes the following sections:

• File: Specifies the file path where the error occurred.
• Current Errors: Lists the errors, marking new errors with a red marker (🔴).
• Suggested Fixes: Provides clear, actionable solutions for each error.
• Audit Trail: Tracks the history of the issue, including timestamps and run IDs.
• Resolution Notes: Documents the fixes applied, including commit SHAs and sign-offs.

Marking New Errors

When a new error is detected, it should be marked as 🔴 unread in the issue body. This visual indicator helps developers quickly identify and prioritize newly introduced issues. The red marker serves as a clear signal that the error requires immediate attention. By highlighting new errors, the automation process ensures that no issues are overlooked, and developers can focus on addressing them promptly.

Assigning Issues to @copilot

Every issue, whether newly created or updated, must be assigned to @copilot. If an issue already exists, it is crucial to ensure that @copilot remains assigned. This assignment ensures accountability and helps track the progress of issue resolution. Never leave an issue unassigned. @copilot serves as the designated entity responsible for overseeing the resolution of linting and compilation errors, providing a clear point of contact and ownership.

3. Resolution Validation

Resolution validation is a critical step in the automated lint issue detection process. It involves verifying whether previously identified errors have been resolved in subsequent runs. This ensures that fixes are effective and that the codebase maintains a high level of quality. The validation process includes confirming that the error no longer appears in the logs, verifying the fix in the file, and updating the issue with resolution notes.

Confirming Error Resolution

In subsequent runs, if an error that was previously detected no longer appears in the build logs, it indicates that a potential fix has been applied. However, it is crucial to confirm this by reading the file itself to ensure that the fix was indeed implemented correctly. This step prevents false positives and ensures that the issue has been genuinely resolved. For instance, an error might disappear from the logs due to a temporary workaround rather than a permanent fix. By inspecting the file, the automation process can verify the integrity of the solution.

Adding Resolution Comments

Once the fix is verified, a comment should be added to the issue stating: "✅ Resolved by @copilot" followed by a summary of the fix. This comment serves as an acknowledgment of the resolution and provides a concise overview of the changes made. The inclusion of @copilot in the comment provides a clear sign-off, indicating that the resolution has been validated by the automated system. This ensures accountability and transparency in the resolution process.

Moving Errors to Resolution Notes

After confirming the resolution, the error should be moved from the "Current Errors" section to the "Resolution Notes" section in the issue body. This categorization helps maintain a clear distinction between active and resolved issues. The "Resolution Notes" section acts as an archive of completed fixes, providing a historical record of the issue resolution process. This ensures that developers can easily track the progress of their fixes and refer back to previous resolutions for context.

Closing or Marking Issues as Resolved

Finally, the issue should be either closed or marked as resolved, depending on the workflow preferences. Closing the issue signifies that the problem has been completely addressed and no further action is required. Marking the issue as resolved achieves a similar outcome but may keep the issue visible for auditing or reference purposes. Both actions indicate that the error has been successfully resolved, preventing it from being overlooked in future reviews.

4. Audit Trail

Maintaining a comprehensive audit trail is essential for tracking the lifecycle of each issue and ensuring accountability. Every issue and update must include timestamps and run IDs, providing a chronological record of the automated lint issue detection process. This audit trail allows developers and project managers to monitor the progress of issue resolution, identify trends, and ensure that all errors are addressed effectively. The detailed tracking helps maintain the integrity and transparency of the code quality assurance process.

Timestamps and Run IDs

Timestamps indicate when an issue was first detected and when it was last updated. This information is crucial for understanding the age of an issue and the frequency of updates. Run IDs link each issue and update to a specific execution of the automated linting process, allowing for easy correlation between log entries and GitHub issues. By including both timestamps and run IDs, the audit trail provides a detailed history of each issue, facilitating thorough analysis and reporting.

Signing Off as @copilot

Every resolution confirmation must be signed off as @copilot. This ensures that the automated system is clearly identified as the entity validating the fix. The sign-off provides an additional layer of accountability, demonstrating that the resolution has been verified by an automated process. It also helps distinguish automated resolutions from manual fixes, making it easier to track the effectiveness of the automated lint issue detection system.

5. Post-Run Review

After completing the issue creation and updates, a post-run review is essential to ensure the integrity and accuracy of the automated process. This self-audit step involves verifying several key aspects of the run to identify and correct any discrepancies. The post-run review ensures that all errors have been captured, issues are correctly assigned, duplicates are avoided, and resolutions are properly documented.

Key Verification Steps

The post-run review includes the following verification steps:

• Every error from the logs was captured into an issue: This ensures that no errors were missed during the parsing and issue creation process. It requires cross-referencing the error logs with the created GitHub issues to confirm that each error is represented.
• Each issue is assigned to @copilot: This verifies that all issues have been correctly assigned, ensuring accountability and proper tracking. A quick review of the issue assignments can identify any issues that may have been overlooked.
• No duplicate issues exist for the same file: This prevents confusion and ensures that effort is not duplicated. The issue titles, which are based on file paths, can be reviewed to confirm that no duplicates exist.
• New errors are marked as 🔴 unread: This ensures that newly detected issues are easily identifiable and prioritized. A review of the issue bodies can verify that new errors are correctly marked.
• Resolved errors were moved to Resolution Notes and signed off: This confirms that the resolution process was correctly followed, and all resolved errors are properly documented. The "Resolution Notes" sections in the issues should be reviewed for completeness and accuracy.

Summary Comment on Workflow Run or PR

After the post-run review, a summary comment should be posted on the workflow run or Pull Request (PR). This comment provides a high-level overview of the run, including:

• Total errors detected: The total number of errors identified in the run.
• Files updated: The number of files that had issues and were updated.
• Issues created vs. updated: The number of new issues created and existing issues updated.
• Errors resolved this run: The number of errors that were resolved during the run.

This summary provides a concise snapshot of the run's outcomes, making it easy to assess the overall impact of the automated lint issue detection process. It also serves as a reference point for future analysis and improvement.

Flagging Discrepancies

If any of the verification checks fail, the discrepancy must be flagged in the summary comment. This ensures that any issues with the automated process are promptly addressed. For example, if an error was detected in the logs but no corresponding issue was created, this should be noted in the summary comment. Flagging discrepancies helps maintain the reliability and effectiveness of the automated system.

📄 Standard Issue Body Template

## 📂 File
<folder>/<file>.ts

---

## ❌ Current Errors
(Each error listed with a red marker if new/unread, or plain if already tracked.)

- 🔴 **<rule>** at line <line>: <message>
- 🔴 **<rule>** at line <line>: <message>

---

## 🛠 Suggested Fixes
For each error, provide a clear, actionable solution:

- **<rule>** → <suggested fix, e.g. remove unused variable, replace `any` with inferred type, import missing module>
- **<rule>** → <fix explanation with link to ESLint docs>

---

## 📑 Audit Trail
- First detected: <timestamp> (Run ID: `<run-id>`)
- Last updated: <timestamp> (Run ID: `<run-id>`)
- Status: `Unread` | `In Progress` | `Resolved`

---

## ✅ Resolution Notes
(Only added when error is fixed)

- ✔ <rule> at line <line> resolved in commit `<sha>`
- Signed off by @copilot

🔄 Update Comment Template (when new errors are detected)

### 🔄 Issue Updated - <timestamp>

### 🆕 New Errors Detected (<count>)
- ⚠️ **NEW:** Line <line>:<col> - <rule> → <message>

### ✅ Resolved Errors (<count>)
- ✔ Line <line>:<col> - <rule> → <message>

**Summary:**
- Previous violations: <old-count>
- Current violations: <new-count>
- Net change: +<added> / -<resolved>

---
*Updated automatically by ClearView Lint Automation*
[Labels applied: `unread-updates`]

✅ Resolution Comment Template (when all errors are fixed)

### ✅ Issue Resolved - All Lint Errors Fixed!

**File:** <folder>/<file>.ts
**Resolved:** <timestamp>

### 🎉 Fixed Violations (<count>)
- ✅ Line <line>:<col> - <rule> → <message>

### 💡 Solutions Applied
- <rule>: <summary of fix, e.g. removed unused variable, replaced `any` with proper type, added missing import>

---
🤖 **Verified and signed off by @copilot**
All lint errors in this file have been successfully resolved. Great work! 🎊

*Automated by ClearView Lint Automation*
[Issue closed]

Automated lint issue detection is a powerful tool for maintaining code quality. By following these guidelines, you can create a robust system that identifies, tracks, and resolves linting errors efficiently. This leads to a cleaner codebase, fewer bugs, and improved developer productivity. Make sure to explore more about code linting and best practices at ESLint Official Documentation for more in-depth information.