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Linting

Linting is the automated static analysis of source code or configuration against rules to identify potential errors, suspicious patterns, and style or consistency violations.

How 8 Manning books explain it

"Looks Good to Me"

A software tool that analyzes source code to detect stylistic, structural, or potential quality problems without executing the program.

How it is used
Automates low-stakes code checks during development and before code review.
How deep it goes
Defines rule-based fault detection, severity choices, and tool boundaries, while leaving rule policy to each team.
Examples
Uses ESLint checks for console calls, variables, imports, equality, alerts, loops, and unnecessary ternaries.
What you can do after
Apply linting via ESLint to detect and fix recurring code problems before review.

Read how "Looks Good to Me" explains linting →

Infrastructure as Code, Patterns and Practices: With examples in Python and Terraform

Linting is the automated analysis of code or configuration to detect nonstandard style, invalid constructs, and incorrect configuration.

How it is used
Catches invalid infrastructure fields, syntax, style, and security violations before code is committed or executed.
How deep it goes
Defines automated style and configuration checks, then connects them to hooks, extensions, CI, and shared repository standards.
Examples
Uses an ip versus ip_address field error, YAML spacing checks, pre-commit hooks, and shared prototype-module tests.
What you can do after
Diagnose configuration mistakes early and enforce shared style, naming, security, and conformance rules automatically.

Read how Infrastructure as Code, Patterns and Practices: With examples in Python and Terraform explains linting →

The Design of Web APIs, Second Edition

A linter is a static-analysis tool that examines source or specification documents for errors, inconsistencies, and possible improvements.

How it is used
Automates API design guidelines while checking OpenAPI definitions during design and development.
How deep it goes
Explains detection, feedback, integration, rule automation, and limitations, but stops short of detailed rule implementation.
Examples
Uses casing errors, missing pagination, undefined scopes, duplicated parameters, and inline schemas as concrete lint findings.
What you can do after
Diagnose API design and OpenAPI authoring problems, then apply guideline-based corrections before implementation or breaking changes.

Read how The Design of Web APIs, Second Edition explains linting →

Essential TypeScript 5, Third Edition

A linter is a software tool that analyzes source code against configurable rules to identify problems or undesirable patterns.

How it is used
Finds confusing patterns and common coding errors that TypeScript’s compiler does not detect.
How deep it goes
Walks through package installation, configuration, diagnostics, and rule exceptions rather than linter internals.
Examples
Uses `prefer-const` and `no-debugger` diagnostics, a `.eslintrc` rule override, and an inline `eslint-disable-line` comment.
What you can do after
Apply it via ESLint and typescript-eslint to check TypeScript files and tailor rules to a project.

Read how Essential TypeScript 5, Third Edition explains linting →

Shipping Go: Develop, deliver, discuss, design, and go again

A linter is a software tool that analyzes source code for programming errors, suspicious constructs, and style violations.

How it is used
Enforces code-quality and style rules as part of preparing changes for repository workflows.
How deep it goes
Defines linters’ role and links them to idiomatic Go, but does not detail specific linter rules or configuration.
Examples
Uses Go formatting and pipeline checks to illustrate automated enforcement alongside linting.
What you can do after
Evaluate how automated checks can enforce code-quality and style rules before repository changes proceed.

Read how Shipping Go: Develop, deliver, discuss, design, and go again explains linting →

React in Depth

Linting is the automated analysis of source code to identify coding errors, undesirable practices, and stylistic violations.

How it is used
Uses automated checks to reduce coding errors and enforce consistent style in growing or collaborative projects.
How deep it goes
Defines linting and illustrates rule categories, but does not detail ESLint configuration or custom rule implementation.
Examples
Uses duplicate object keys, inconsistent return types, and missing final newlines as representative linting targets.
What you can do after
Apply ESLint-style checks to enforce conventions and reduce minor errors in source code.

Read how React in Depth explains linting →

Pro Angular 16

A linter is a static-analysis tool that examines source code for programming errors, style violations, and other rule-defined issues.

How it is used
Uses static analysis to catch code problems before version-control commits or other development processes.
How deep it goes
Explains installation, execution, diagnostics, suppressions, and rule configuration without detailing linter internals.
Examples
Demonstrates `debugger`, Angular selector casing, `ng lint` output, suppression comments, and `.eslintrc.json` rules.
What you can do after
Diagnose with `ng lint`, interpret rule violations, and configure or narrowly suppress unavoidable findings.

Read how Pro Angular 16 explains linting →

Build Financial Software with Generative AI (From Scratch)

A linter is a static-analysis tool that detects style violations, common programming mistakes, and other suspicious patterns in source code.

How it is used
Reduces cognitive load and enforces consistent coding standards as development teams and codebases grow.
How deep it goes
Defines the role of linters and their benefits but stops short of configuration, rule design, or implementation procedures.
Examples
Names SonarLint alongside Python black, Perl perltidy, and Go gofmt as tools that impose coding structure.
What you can do after
Apply it via a linter to enforce standards and identify problems in source or generated code.

Read how Build Financial Software with Generative AI (From Scratch) explains linting →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.