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This guide walks through installing HttpRunner and Locust, creating a simple YAML test case, converting it to a Locust script, running load tests, customizing behavior, and analyzing performance metrics to evaluate API reliability under load.
This article introduces the open‑source HttpRunner API testing tool, outlines its key features such as YAML/JSON test definitions, parameterization, assertions, reporting and performance testing, and provides step‑by‑step Python examples showing how to write, run, and integrate HttpRunner tests with existing requests‑based projects.
HttpRunner 4.x enables flexible variable declaration and usage at both configuration and test-step levels, supporting $ and {{}} syntax, dynamic assignment from responses, parameterization, request customization, extraction, custom functions, and validation, allowing more maintainable and powerful API test cases.
This article explains how the extract and validate features of HttpRunner can be used to retrieve data from HTTP responses and assert response correctness, providing multiple practical code examples for token handling, pagination, response time checks, and custom validation functions in API testing.
This article explains the hierarchical structure of HttpRunner 4.x test cases, detailing configuration, parameters, test steps, variables, extraction, validation, hooks, dependencies, and reuse features for effective API automation testing, and provides practical YAML/JSON examples to illustrate each component.
This article explains the importance of API automation testing, outlines why it is needed for complex supply‑chain systems, describes the step‑by‑step implementation using HttpRunner, and details subsequent optimizations such as mock service validation, scheduled inspections, execution time reduction, self‑service pipelines, and future planning.
This article explains how software test engineers can combine ChatGPT and the HttpRunner framework to address DevOps testing pain points, record web operations, batch‑modify scripts, run and debug tests, and automatically generate API test cases and scripts for faster, more efficient regression testing.
This article explores how software test engineers can leverage ChatGPT to improve efficiency across the testing workflow, including requirement analysis, test case creation, defect reporting, functional and API test generation, and automation script development, while providing code examples and practical tips for integrating AI assistance into testing processes.
This article introduces the concept and necessity of automated API testing, outlines typical usage scenarios, and provides a detailed comparison of four popular frameworks—Postman + Newman, JMeter + Ant, HttpRunner + Request, and pytest + Request—covering their features, advantages, drawbacks, requirements, and integration steps.
This article summarizes practical problems encountered with HttpRunner—such as YAML parsing errors, missing attributes, duplicate assertions, custom function parameter issues, and performance bottlenecks—and provides detailed debugging steps, code patches, multithreading enhancements, and report‑optimization techniques for large‑scale API test suites.
This article introduces HttpRunner 2.0.1 for API automation, explains the environment setup, project structure, essential CLI commands and Python integration, and shares solutions to frequent problems such as timestamp handling, variable overriding, extraction, and execution repeats, along with useful tips for efficient test layering.
This article introduces HttpRunner, a universal HTTP(S) testing framework, explains the YAML/JSON structure for APIs and test cases, demonstrates GET and POST definitions, and shares practical tips on handling variables, output, and extract to avoid common pitfalls in automated API testing.
This article introduces HttpRunner for API automation, covering environment setup, project structure, core commands, execution methods, workflow, and a collection of practical problems and solutions encountered during real‑world usage.
This article introduces HttpRunner, covering supported platforms, installation commands, key features such as parameter extraction, global configuration, variable handling, data-driven testing, report generation, and practical steps for converting HAR files to test cases and executing them with hrun.
This guide introduces HttpRunner, covering supported operating systems, Python environment setup, installation via pip, key commands, parameter correlation, global configuration, variable handling, data‑driven testing, report generation, and a practical workflow using HAR files to create and run test cases.