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Allure 3: Getting Started

Install & Upgrade

Install for Node.js

Upgrade Allure

Working With Reports

How to generate a report

How to view a report

Improving readability of your test reports

Improving navigation in your test report

Allure 2: Getting Started

Install & Upgrade

Install for Windows

Install for macOS

Install for Linux

Install for Node.js

Upgrade Allure

Working With Reports

How to generate a report

How to view a report

Improving readability of your test reports

Improving navigation in your test report

Features

Test steps

Attachments

Test statuses

Sorting and filtering

Defect categories

Visual analytics

Test stability analysis

History and retries

Timeline

Export to CSV

Export metrics

Guides

JUnit 5 parametrization

JUnit 5 & Selenide: screenshots and attachments

JUnit 5 & Selenium: screenshots and attachments

Setting up JUnit 5 with GitHub Actions

Pytest parameterization

Pytest & Selenium: screenshots and attachments

Pytest & Playwright: screenshots and attachments

Pytest & Playwright: videos

Playwright parameterization

Allure Report 3: XCResults Reader

How it works

Overview

Test result file

Container file

Categories file

Environment file

Executor file

History files

Integrations

Azure DevOps

Bamboo

GitHub Actions

Jenkins

JetBrains IDEs

TeamCity

Visual Studio Code

Frameworks

Behat

Getting started

Configuration

Reference

Behave

Getting started

Configuration

Reference

Codeception

Getting started

Configuration

Reference

CodeceptJS

Getting started

Configuration

Reference

Cucumber.js

Getting started

Configuration

Reference

Cucumber-JVM

Getting started

Configuration

Reference

Cucumber.rb

Getting started

Configuration

Reference

Cypress

Getting started

Configuration

Reference

Jasmine

Getting started

Configuration

Reference

JBehave

Getting started

Configuration

Reference

Jest

Getting started

Configuration

Reference

JUnit 4

Getting started

Configuration

Reference

JUnit 5

Getting started

Configuration

Reference

Mocha

Getting started

Configuration

Reference

Newman

Getting started

Configuration

Reference

NUnit

Getting started

Configuration

Reference

PHPUnit

Getting started

Configuration

Reference

Playwright

Getting started

Configuration

Reference

pytest

Getting started

Configuration

Reference

Pytest-BDD

Getting started

Configuration

Reference

Reqnroll

Getting started

Configuration

Reference

REST Assured

Getting started

Configuration

Robot Framework

Getting started

Configuration

Reference

RSpec

Getting started

Configuration

Reference

SpecFlow

Getting started

Configuration

Reference

Spock

Getting started

Configuration

Reference

TestNG

Getting started

Configuration

Reference

Vitest

Getting started

Configuration

Reference

WebdriverIO

Getting started

Configuration

Reference

xUnit.net

Getting started

Configuration

Reference

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Tianjun Liu - Chines.. Apr 2026

: He investigates how e-commerce adoption impacts selling prices for apple farmers, finding that digital platforms increase market flexibility and benefit smaller, less-educated rural households significantly.

Tianjun Liu, associated with Northwest A&F University , specializes in the intersection of traditional agricultural production and modern digital factor markets. Tianjun Liu - Chines..

Tianjun Liu is a prominent Chinese researcher whose work bridges the gap between and advanced deep learning technologies . His research focus is particularly strong in the digital transformation of China's rural economy and the application of AI in agricultural food systems. Research Focus and Core Expertise : He investigates how e-commerce adoption impacts selling

: His research includes the ED-DenseNet model, which enhances deep feature extraction through multi-branch structures and ECA attention mechanisms, achieving a 97.82% recognition accuracy in gas-liquid flow patterns. His research focus is particularly strong in the

: His technical work includes "Deep Learning in Food Image Recognition," exploring multi-branch structures for high-accuracy feature extraction.

: He has mapped significant growth areas for apple production across provinces like Gansu, Shaanxi, and Henan, identifying fertilizer machinery input as a key efficiency factor. Deep Feature Extraction Research

: He has proposed urban big data classification methods using lightweight deep learning (LWT-DL) to improve the security and efficiency of smart city construction.

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