What’s a Linter VS AI Linter and Why do they Matter?

If you build software without a linter, you’re gonna have a bad time.

A linter is an essential tool developers use, but what exactly is a linter, and how does it help ICs daily?

What is a Linter?

A linter is a static code analysis tool that scans your source code to identify potential errors, bugs, stylistic errors, and suspicious constructs.

Fun fact: the term “linting” originated from a Unix utility called lint that flagged suspicious and non-portable constructs in C language code.

Today, linters are available for almost every programming language, some frameworks like React, and they play a pivotal role in modern software development - tabs not spaces, friends.

How Linters Help Developers

  1. Error Detection: Linters catch common programming errors like: syntax errors, undeclared variables, and incorrect function usage. By catching these issues early in the development process, linters prevent potential runtime errors and bugs, saving developers from frustrating debugging sessions later on.
  2. Code Consistency: Linters enforce coding standards and style guidelines, ensuring that the codebase remains consistent. Particularly useful in teams where multiple developers work on the same project to keep a uniform coding style, making the code easier to read and understand.
  3. Improved Code Quality: By highlighting code smells, redundant code, and other suboptimal coding practices, linters encourage developers to write cleaner, more efficient code.
  4. Time Savings: Linters help automate parts of code review by checking for compliance with predefined coding standards. This reduces the manual effort required in peer code reviews, allowing developers to focus on more critical aspects of the review process.
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p.s. Squire performs entire code reviews for you, check it out.
  1. Continuous Integration: Linters can be integrated into continuous integration (CI) pipelines, ensuring that only code meeting quality standards is merged into the main codebase.

Real-Life Scenario

Let’s take a run at this with Fictional Jane, a junior developer working on a Python project: 

Without a linter, Jane might struggle to adhere to the coding standards of her team, like remembering tabs vs spaces and potentially introducing errors and inconsistencies. By using a Python linter like Pylint, Jane receives feedback on her code in IDE, learns from mistakes, and contributes higher quality code to the project now and overtime.

The linter also saves her time by catching errors early, allowing her to focus on developing new features.

This section explains what linters are and how they benefit individual contributors. Let me know if you need any adjustments or if you’re ready to move on to the next part about introducing the concept of an AI Linter.

btw, we run ESLint (a great linter) & Prettier (not a linter, but a formatter) here at Squire if you're looking for a reference.

AI Linter: better code through "intelligent" analysis

As software development evolves, so too must the tools we use to ensure code quality. Enter the AI Linter – designed to take static code analysis to a new level. By leveraging LLMs and agentic workflows an AI Linter not only catches errors and enforces coding standards but also employs complex reasoning to understand the deeper context and intent behind the code.

What is an AI Linter?

An AI Linter is an advanced static code analysis tool powered by artificial intelligence and machine learning. Unlike traditional linters, which rely on predefined rules and patterns to detect issues, an AI Linter learns from vast amounts of code data, team interactions, and uses algorithms to identify subtle and complex problems that conventional tools might miss.

How an AI Linter Enhances Error Detection and Code Quality

  1. Contextual Understanding: An AI Linter goes beyond syntax and style checks. It understands the context in which the code is written, including variable scopes, function calls, and dependencies. This allows it to identify logical errors, potential bugs, and inefficiencies that are not apparent from surface-level analysis.
  2. Context Aware: With an understanding of your codebase, an AI Linter learns best practices and common pitfalls across different repositories, languages, and frameworks. Providing more accurate and relevant feedback, tailored to the specific coding environment, how the team writes code, and project requirements.
  3. Dynamic Rule Generation: Traditional linters use static rules, which can become outdated or fail to cover new coding patterns. An AI Linter, however, can dynamically generate and update its rules based on continuous learning from new code commits and developer feedback, ensuring it remains effective as coding standards and practices evolve.
  4. Complex Bug Detection: AI-driven analysis can uncover complex bugs related to concurrency, race conditions, memory leaks, and performance bottlenecks. These issues often require deep understanding and are challenging for traditional linters to detect.
  5. Automated Code Refactoring: An AI Linter can suggest and even automate code refactoring to improve readability, performance, and maintainability. By recognizing code smells and suboptimal patterns, it helps developers write cleaner and more efficient code with far less manual intervention.
  6. Intelligent Suggestions: Beyond merely flagging issues, an AI Linter provides intelligent suggestions and explanations. It not only tells developers what’s wrong but also why it’s wrong and how to fix it. Helping developers improve their skills and understand the reasoning behind best practices.

Real-Life Scenario

Here comes Fictional Alex, a full-stack developer working on a complex microservices architecture. Shoulda gone monorepo, but whatever Alex.

Traditional linters help Alex catch syntax errors and enforce style guidelines, but they often fall short when it comes to identifying deep-seated logical errors and performance issues.

With an AI Linter, Alex receives insights that go far beyond surface-level checks. For example, the AI Linter detects a potential deadlock in a multithreaded service, suggests optimizations for database query performance, and flags a subtle bug in the data processing pipeline that could lead to data corruption. By providing these advanced insights, the AI Linter saves Alex hours of debugging and ensures that the application runs smoothly and efficiently.

Even Fictional Juan, the site-reliability engineer, appreciates it - since they didn’t get paged at 1am for a fix.

Getting Started with Squire’s AI Linting

Integrating Squire’s AI Linter into your development workflow is straightforward:

  1. Sign up on our website - 30 seconds
  2. install the GitHub application - 15 seconds
  3. drop a squire.config.json in your root with custom instructions - 2 mins to copy pasta & first ship
  4. See it in action on your next code review from Squire

Our AI Linter supports all programming languages and can be customized to fit the specific needs of your project.

Deterministic VS Opinionated systems

Linters & AI Linters both belong in your software delivery lifecycle, full stop.

Linters are wonderful and deterministic in how they support you & teams in maintaining a healthy codebase with a mix of standards, your configurations, and preferences.

AI Linters leverage LLMs and agentic workflows, which are inherently non-deterministic and opinionated. The downside is an opinionated system can “miss” things or direct attention to something benign depending on the rules & configurations you’ve given it. It’s important to iterate on your settings, treat them like prompts and rework them as you go.

Having both systems in production lifts the overall team’s velocity of code to production, improves code quality, and catches a lot more before it falls through the cracks.