Linting Baseline & CI Regression Checks

Hey there, code enthusiasts! Let's talk about keeping our code squeaky clean and preventing those pesky quality issues from creeping in. We're diving into a crucial process: establishing a lint warning baseline and setting up CI (Continuous Integration) regression checks. This is all about preventing code quality degradation and ensuring our projects stay top-notch. Sounds good? Let's jump in!

Setting the Stage: The Need for a Linting Baseline

The Problem with Lint Warnings

So, why are we even bothering with this? Well, picture this: your project is growing, and with it, the potential for code quality issues. Lint warnings, while not always blocking, can be a real pain. They create review noise, making it harder to spot the important stuff. They can also hide potential bugs within the noise, leading to unexpected behavior down the road. And let's not forget the gradual erosion of code quality, like a slow leak in a tire. It's not immediately catastrophic, but it will eventually cause problems. We're looking at 763 lint warnings, along with one error, in our latest release. This isn't ideal, right?

Top Offenders and What They Mean

We've identified some top offenders that are contributing most to these warnings. They're like the usual suspects in a detective story. The biggest culprits include: @typescript-eslint/no-unsafe-* (unsafe any usage), @typescript-eslint/strict-boolean-expressions (nullable checks), and @typescript-eslint/no-unused-vars (unused imports). These rules are there for a reason – to help catch potential errors and enforce best practices. The problem files include policy/scanners/test_scanners.ts, shared/cli/*.ts, and memory/renderer/*.ts. By tackling these, we can make some serious improvements.

The Goal: Preventing Quality Erosion

Our main goal here is straightforward: preventing quality erosion. We want to make sure that our code doesn't slowly degrade over time. The linting baseline and CI regression checks are our tools to achieve this. By setting a baseline, we know where we stand, and by comparing current warnings against that baseline, we can identify any regressions – any increases in warnings – that indicate a potential problem.

The Action Plan: How We'll Achieve Our Goal

Creating the Linting Baseline

The first step is to generate our baseline. This involves running our linter and capturing all the warnings in a JSON format. This baseline is our starting point and the reference against which we'll compare future linting runs. The command will be npm run lint --format=json > lint-baseline.json. This will create a lint-baseline.json file. This file then becomes the historical record of the quality of our code at a given time.

Setting Up CI Regression Checks

Next, we'll integrate this into our CI pipeline. This will be an automated process that runs every time we push new code. The CI job will compare the current lint warnings with our baseline and fail the build if the warning count increases. This is the heart of our regression check. We'll be using a new CI job and a comparison script.

Detailed Steps: Files and Algorithm

The implementation involves creating or modifying several files. Specifically, we'll need .github/workflows/lint-budget.yml for the new CI job, scripts/lint-budget.mjs for the comparison script, and lint-baseline.json for the baseline file itself. The script will generate the current warnings and compare them to the baseline, which will be the basis for quality control.

Here's how the algorithm works:

1. Generate current warnings: We'll use npm run lint --format=json > current.json. This generates a JSON file with the current lint warnings.
2. Compare: We'll run node scripts/lint-budget.mjs current.json lint-baseline.json. This script compares the current.json file to the lint-baseline.json file.
3. Exit code: The script will exit with code 1 if there's a regression (i.e., the warning count has increased). This signals to the CI pipeline that the build should fail.

Reporting and Documentation

Our CI job will also provide useful reports. Specifically, it will identify the top 10 offenders by file and rule. This helps us focus our efforts on the most problematic areas. We will also update our CONTRIBUTING.md file to include a lint budget section so that new contributors can understand how linting works and what to expect.

Success Metrics and Future Goals

Defining Success

We'll consider this initiative successful if our CI pipeline fails when the number of warnings increases. In addition to this, the report should include the top 10 offenders by file and rule. The goal is to catch any regressions early on. This will help us prevent quality erosion.

Reducing the Number of Warnings

As a follow-up, our target is to reduce the number of warnings in the top 5 files by 50%. This will be a more manual task, as it involves fixing the code that generates those warnings. The reduction of warnings will improve the overall code quality.

Priority and Effort

This project has been assigned high priority (P0) because it directly impacts code quality. The effort required is medium (M), as it requires some scripting and CI integration.

Conclusion: Keeping it Clean

And that's the plan, folks! By establishing a linting baseline and integrating CI regression checks, we're taking a proactive approach to maintain code quality. This isn't just about avoiding errors; it's about making our code easier to read, understand, and maintain. It's about ensuring our project remains robust and scalable as it grows. The end result is a cleaner, more reliable codebase, and less headache for everyone involved. So, let's get to it and keep that code sparkling!