Prompt

Jenkins pipeline for tests and deployment

Technology / CI/CD & DevOps1 observationLast seen Oct 19, 2026

Latest observation

Oct 19, 2026OpenAI APIWeb search: off

Here’s a basic Jenkins Declarative Pipeline for running tests and then deploying if tests pass.

Example Jenkinsfile

pipeline {
    agent any

    environment {
        APP_NAME = "my-app"
        DEPLOY_SERVER = "your-server.example.com"
        DEPLOY_PATH = "/var/www/my-app"
    }

    stages {
        stage('Checkout') {
            steps {
                checkout scm
            }
        }

        stage('Install Dependencies') {
            steps {
                sh 'npm install'
            }
        }

        stage('Run Tests') {
            steps {
                sh 'npm test'
            }
        }

        stage('Build') {
            steps {
                sh 'npm run build'
            }
        }

        stage('Deploy') {
            when {
                branch 'main'
            }
            steps {
                sh """
                    scp -r dist/* user@${DEPLOY_SERVER}:${DEPLOY_PATH}
                """
            }
        }
    }

    post {
        success {
            echo 'Pipeline completed successfully.'
        }
        failure {
            echo 'Pipeline failed.'
        }
        always {
            cleanWs()
        }
    }
}

What this does

  • Checkout: pulls code from SCM
  • Install Dependencies: installs project dependencies
  • Run Tests: executes automated tests
  • Build: builds the app
  • Deploy: deploys only from the main branch
  • Post actions: logs status and cleans workspace

Common improvements

  • Use credentials binding instead of hardcoding server/user info
  • Replace npm commands with your stack:
    • Maven: mvn test, mvn package
    • Python: pytest
    • Java: gradle test
  • Add staging and production deployment stages
  • Use SSH keys or Docker/Kubernetes deployment steps depending on environment

If you want, I can give you:

  1. a Java/Maven Jenkins pipeline,
  2. a Python pipeline, or
  3. a Docker/Kubernetes deployment version.

Observation history

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