This guide explains how to integrate ESLint results into your Bitbucket Pull Requests using Bitbucket Pipelines. You'll learn how to generate ESLint reports in JSON format, post these as inline annotations using the Bitbucket Reports and Annotations API, and configure a Bitbucket pipeline to run ESLint automatically.
Generate ESLint Report as JSON
First, you'll need to run ESLint and output the results in JSON format. This file will later be used to create a report and annotations.
Add -f and -o args to your eslint command. E.g:
eslint . --ext .ts -f json -o eslint-report.json
Post ESLint Report and Annotations to Bitbucket
To display ESLint findings directly in your Pull Requests, you'll use Bitbucket's Report API and Annotations API.
- Read the ESLint JSON report.
- Generate a report with the total number of errors and warnings.
- Post inline annotations based on ESLint messages.
const fs = require('fs') const path = require('path') const util = require('util') // External ID must be unique per report on a commit const EXTERNAL_ID = 'com.yorcompany.reports.eslint' const BB_USER = 'YOUR_USER' const BB_REPO = 'YOUR_REPO' const BB_URL = 'https://api.bitbucket.org/2.0' // This is available by default in the pipeline. const COMMIT = process.env.BITBUCKET_COMMIT // For this to be availble you need to create an access token with read access to the repo // and set it an environment variable in the pipeline. const TOKEN = process.env.BITBUCKET_TOKEN // Map ESLint severities to Bitbucket report severities const severities = { 0: 'LOW', 1: 'MEDIUM', 2: 'HIGH' } // Read the ESLint JSON report const data = await util.promisify(fs.readFile)(path.join(process.cwd(), 'eslint-report.json'), 'utf8') .catch(err => { console.error('Error reading eslint-report.json:', err) throw err }) const eslintOutput = JSON.parse(data) let totalErrorCount = 0 let totalWarningCount = 0 const annotations = [] let i = 1 eslintOutput.forEach(file => { totalErrorCount += file.errorCount totalWarningCount += file.warningCount const relativePath = path.relative(process.cwd(), file.filePath) file.messages.forEach(message => { annotations.push({ external_id: `${EXTERNAL_ID}.${COMMIT}.${i++}`, path: relativePath, annotation_type: 'CODE_SMELL', summary: message.message, line: message.line, severity: severities[message.severity] }) }) }) // Prepare the report const report = { title: 'ESLint Report', details: 'Results from ESLint analysis', report_type: 'TEST', logoUrl: 'https://eslint.org/img/logo.svg', data: [ { title: 'Error Count', type: 'NUMBER', value: totalErrorCount }, { title: 'Warning Count', type: 'NUMBER', value: totalWarningCount } ] } try { // Post the report to Bitbucket const reportUrl = `${BB_URL}/repositories/${BB_USER}/${BB_REPO}/commit/${COMMIT}/reports/${EXTERNAL_ID}` let response = await fetch(reportUrl, { method: 'PUT', body: JSON.stringify(report), headers: { 'Content-Type': 'application/json', 'Accept': 'application/json', 'Authorization': `Bearer ${TOKEN}` } }) if (!response.ok) { console.error(await response.text()) throw new Error(`Error posting report: ${response.statusText}`) } console.log('Report posted successfully!') console.log(await response.json()) // Post annotations if any if (annotations.length > 0) { const annotationsUrl = `${BB_URL}/repositories/${BB_USER}/${BB_REPO}/commit/${COMMIT}/reports/${EXTERNAL_ID}/annotations` response = await fetch(annotationsUrl, { method: 'POST', body: JSON.stringify(annotations), headers: { 'Content-Type': 'application/json', 'Accept': 'application/json', 'Authorization': `Bearer ${TOKEN}` } }) if (!response.ok) { console.error(await response.text()) throw new Error(`Error posting annotations: ${response.statusText}`) } console.log('Annotations posted successfully!') console.log(await response.json()) } } catch (error) { console.error('Error posting insights:', error.response ? error.response.data : error.message) }
Configure Bitbucket Pipeline
To automate this process as part of your CI/CD workflow, you can set up a Bitbucket pipeline to run ESLint, generate the JSON report, and post the results. Below is a sample bitbucket-pipelines.yml file to get you started:
image: node:18.13.0 pipelines: default: - step: name: ESLint caches: - node script: - npm install - npx eslint . --ext .ts -f json -o eslint-report.json # Run ESLint and save the report after-script: - node post-eslint-results.js # Post results to Bitbucket artifacts: - eslint-report.json
Note
Report is posted to Bitbucket in after-script because subsequent scripts will not be called if eslint returns non 0 exit code (if ESLint has errors).
The above is the detailed content of Eslint Code Insights from Bitbucket pipelines. For more information, please follow other related articles on the PHP Chinese website!

Python and JavaScript have their own advantages and disadvantages in terms of community, libraries and resources. 1) The Python community is friendly and suitable for beginners, but the front-end development resources are not as rich as JavaScript. 2) Python is powerful in data science and machine learning libraries, while JavaScript is better in front-end development libraries and frameworks. 3) Both have rich learning resources, but Python is suitable for starting with official documents, while JavaScript is better with MDNWebDocs. The choice should be based on project needs and personal interests.

The shift from C/C to JavaScript requires adapting to dynamic typing, garbage collection and asynchronous programming. 1) C/C is a statically typed language that requires manual memory management, while JavaScript is dynamically typed and garbage collection is automatically processed. 2) C/C needs to be compiled into machine code, while JavaScript is an interpreted language. 3) JavaScript introduces concepts such as closures, prototype chains and Promise, which enhances flexibility and asynchronous programming capabilities.

Different JavaScript engines have different effects when parsing and executing JavaScript code, because the implementation principles and optimization strategies of each engine differ. 1. Lexical analysis: convert source code into lexical unit. 2. Grammar analysis: Generate an abstract syntax tree. 3. Optimization and compilation: Generate machine code through the JIT compiler. 4. Execute: Run the machine code. V8 engine optimizes through instant compilation and hidden class, SpiderMonkey uses a type inference system, resulting in different performance performance on the same code.

JavaScript's applications in the real world include server-side programming, mobile application development and Internet of Things control: 1. Server-side programming is realized through Node.js, suitable for high concurrent request processing. 2. Mobile application development is carried out through ReactNative and supports cross-platform deployment. 3. Used for IoT device control through Johnny-Five library, suitable for hardware interaction.

I built a functional multi-tenant SaaS application (an EdTech app) with your everyday tech tool and you can do the same. First, what’s a multi-tenant SaaS application? Multi-tenant SaaS applications let you serve multiple customers from a sing

This article demonstrates frontend integration with a backend secured by Permit, building a functional EdTech SaaS application using Next.js. The frontend fetches user permissions to control UI visibility and ensures API requests adhere to role-base

JavaScript is the core language of modern web development and is widely used for its diversity and flexibility. 1) Front-end development: build dynamic web pages and single-page applications through DOM operations and modern frameworks (such as React, Vue.js, Angular). 2) Server-side development: Node.js uses a non-blocking I/O model to handle high concurrency and real-time applications. 3) Mobile and desktop application development: cross-platform development is realized through ReactNative and Electron to improve development efficiency.

The latest trends in JavaScript include the rise of TypeScript, the popularity of modern frameworks and libraries, and the application of WebAssembly. Future prospects cover more powerful type systems, the development of server-side JavaScript, the expansion of artificial intelligence and machine learning, and the potential of IoT and edge computing.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

SublimeText3 Chinese version
Chinese version, very easy to use

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.

Dreamweaver Mac version
Visual web development tools

Safe Exam Browser
Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.