This guide explains how to serialize and deserialize Python objects using the pickle, json, and xml.etree.ElementTree modules, and demonstrates practical API testing scenarios—including POST, GET, PUT, DELETE, and nested JSON handling—so you can choose the right format for your needs.
This article explains how to define token retrieval methods as pytest fixtures to reuse authentication tokens across multiple test cases, ensuring fresh tokens for each test and demonstrating fixture scope, cleanup, and global usage patterns.
This guide explains how to identify, store, and share data dependencies, control test execution order, handle failure propagation, visualize relationships, and avoid excessive coupling in API automated test suites, using Python examples and tools like pytest and Graphviz.
This article details the evolution of a high‑traffic API automation testing platform, covering challenges such as multi‑environment isolation, execution speed, quality assessment, and stability, and explains how the team leveraged Kubernetes, Rancher, Tekton, precise testing, and modern reporting to dramatically improve efficiency and reliability.
This article demonstrates how to implement data-driven API testing in Python using pytest framework with YAML files for test data and environment configuration, providing a flexible and maintainable testing solution.
This guide outlines a step‑by‑step method for recognizing critical APIs, evaluating their importance and complexity, assigning priority levels, and preparing the necessary people, tools, and CI/CD processes to implement effective API automation testing.
This guide walks through ten practical Python examples for parsing, constructing, validating, updating, deleting, and iterating JSON data via HTTP requests, including custom date serialization and schema validation, each demonstrated with complete code and expected outcomes.
This guide demonstrates how parameterized Python decorators can streamline repetitive tasks in API automation testing—handling login tokens, retries, environment selection, data‑driven tests, logging, performance monitoring, permission checks, mock responses, cleanup, and test tagging—reducing code duplication and improving maintainability.
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 guide explains how to set up httprunner 4.x, write YAML/JSON test cases, run them, use parameterization and environment variables, and generate HTML reports for efficient Python API automation 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 provides a series of Python code examples demonstrating how to perform API automation testing tasks—including GET and POST requests, authentication, file uploads, redirects, unit testing with unittest and pytest, response mocking with responses, and response schema validation with jsonschema—offering practical guidance for backend testing.
This article demonstrates ten practical methods for handling dynamic parameters in Python API automation testing, covering dictionaries, loops, pytest parametrization, fixtures, unittest, decorators, generators, class attributes, reflection, and external data sources like CSV.
Discover a curated list of twelve powerful open‑source tools—from IDEs and API testers to AI model runners and DevOps utilities—that can dramatically boost developer productivity and streamline everyday workflows.
This article presents 10 practical Python scripts that significantly enhance API automation testing efficiency, covering test case generation, data handling, request management, validation, and reporting.
This article explains how to use Go's httptest package and the gock library to mock HTTP servers and external API calls, providing step‑by‑step code examples, test case structures, and installation instructions for effective backend unit testing.
This guide explains how Apipost’s pre‑execution scripts can run before a request, allowing developers to write JavaScript functions, manipulate variables, modify headers, queries, bodies, and invoke external programs via the apt.execute API, with version requirements, usage syntax, and a complete code example.
This article outlines the background, step‑by‑step implementation plan, execution mechanisms, sustainable maintenance practices, and measurable outcomes of introducing API automation testing to ensure high‑quality iterative development and stable online services.
This article presents six practical techniques—hard‑coding, using loops, reading from Excel/CSV, loading YAML/JSON files, leveraging pytest parameterization, and querying databases—to manage and reuse API test parameters in Python, each illustrated with clear code examples.
This guide walks you through the fundamentals of API automation testing, from understanding HTTP basics and selecting tools, to building a Pytest framework, writing test cases and scripts, integrating tests into CI/CD pipelines, and continuously improving test results.
The article outlines practical principles and recommended coverage percentages for functional, boundary, security, performance, regression, integration, data management, and maintainability aspects of API automation testing, explaining why each level of coverage is essential for quality and efficiency.
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 guide explains how to use Apipost’s one‑click load testing feature to evaluate API performance under high concurrency, covering influencing factors, recommended concurrency steps, configuration steps, result analysis, and links to official documentation.
This article demonstrates how to employ HiPlot in API automated testing to efficiently visualize and analyze large sets of test data, covering single-run results, version comparisons, parameter impact studies, long‑running test sequences, and multi‑environment performance evaluations.
This article provides a comprehensive guide to using Python's requests library for API testing, covering basic GET/POST requests, error handling, authentication, and concurrent requests with practical code examples.
This guide explains Python’s built‑in isinstance() function, shows its basic syntax and usage—including single‑type, multiple‑type, and inheritance checks—and provides ten practical API‑testing scenarios with ready‑to‑run code examples.
This article outlines detailed data generation techniques, parameter combination methods, dependency management, performance and fault‑tolerance testing, coverage analysis, and provides a complete pytest‑based Python example for robust API testing.
This article introduces Cool Request, a free open‑source IntelliJ IDEA plugin that automatically extracts SpringBoot controllers, simplifies HTTP request debugging, supports multiple request bodies, pre‑ and post‑scripts, reflection calls, cURL import, Apifox export, global API search, and even provides a static resource server.
This guide reviews five powerful Linux command-line HTTP clients—cURL, HTTPie, Wget, HTTPie-go, and Wuzz—explaining their key features and providing concrete command examples for GET, POST, file downloading, and interactive API testing.
This tutorial demonstrates how to set up a SpringBoot Java service, expose its API through cpolar's internet‑penetration tunnel, configure a fixed sub‑domain, and use the cpolar listener to monitor and replay requests, providing a complete workflow for backend developers.
Deep copy in Python is demonstrated through multiple practical examples—including request parameter duplication, handling nested data structures, managing global configurations, multithreaded test execution, and database interactions—to ensure isolated, independent data for each API test case, preventing data contamination and race conditions.
This article explains how to use Python's built‑in json module for serializing and deserializing data, demonstrates sending JSON payloads with the requests library, and covers advanced techniques such as data‑driven testing, JsonPath/JMESPath extraction, JSON Schema validation, and dynamic request body construction for API automation.
This guide introduces Insomnia, an open‑source, cross‑platform API client that supports GraphQL, REST, WebSockets, and gRPC, walks through its simple installation, demonstrates how to import and test the SpringBoot‑based mall project via Swagger, and highlights useful configuration tips for efficient backend debugging.
This guide demonstrates how to build a Python API automation testing suite using requests, pytest, and pytest‑html, covering environment setup, project structure, common utilities, test case implementation, advanced assertions, error handling, test data management, and HTML report generation.
This guide explains how to use Burp Suite's Auth Analyzer plugin to efficiently perform bulk API access‑control (broken access control) testing, covering vulnerability types, tool installation, step‑by‑step testing procedures, result analysis, and report export for improved software security.
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 guide explains how to use Postman's pre‑request and test scripts to automate request signing, switch between multiple environments, and handle JWT‑based authentication, providing step‑by‑step instructions, execution order details, and practical code snippets.
This article explains why YAML is ideal for managing API test framework configurations, outlines its syntax and structure, and provides concrete examples for global settings, environment-specific parameters, endpoint definitions, best‑practice guidelines, and Python parsing with PyYAML, helping teams build maintainable and scalable test suites.
This article explains Python's multiprocessing module, compares it with multithreading, outlines its benefits for CPU utilization and resource isolation, and provides five practical code examples demonstrating concurrent task execution, parallel calculations, data processing, API requests, and test case automation.
This guide explores Postman's powerful yet often overlooked features—environment and collection variables, pre‑request scripts, and test scripts—showing how to automate API testing, manage multiple environments, and streamline token handling for more efficient backend development.
This article explains how IntelliJ IDEA's built‑in REST Client provides all of Postman's features plus environment configuration, scriptable assertions, and request sharing, making API testing faster and more integrated within the IDE.
This article introduces five common Python automated testing scenarios—unit testing, API testing, web UI testing, performance testing, and database testing—providing clear explanations and complete code examples to help developers improve software quality and stability.
This article compares a Java + TestNG + HttpClient stack with a Groovy + Spock + RestAssured approach, showing how data‑driven testing, encryption handling, data extraction, and validation can be streamlined for more flexible and efficient API testing.
This article presents a comprehensive backend automated testing framework that unifies HTTP and RPC access, introduces a parameter‑pool concept, leverages JSON Schema and JSONPath for validation, and outlines coverage metrics, test case generation, discovery, and continuous improvement to achieve near‑100% API test coverage.
This article explains five common methods for storing authentication tokens during API automated testing—embedding in scripts, using environment variables, configuration files, databases, and secret management systems—and provides complete Python code samples for each approach.
This article explains what ChatGPT is, describes how it can improve various software testing activities such as API, unit, mobile, integration, and end‑to‑end testing, highlights its context‑driven capabilities, and outlines the key limitations that testers should be aware of.
This guide explains the fundamentals of automation testing, identifies suitable projects, compares common automation types such as web, mobile, and API testing, introduces essential tools like Selenium, Appium, uiautomator2, and JMeter, and provides step‑by‑step code examples to help beginners quickly implement automated tests in real‑world scenarios.
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.
The RunnerGo testing platform provides API debugging, automation, and performance testing through a multi‑service online architecture that includes management, engine, collector, front‑end, and various middleware components such as Redis, MySQL, MongoDB, Kafka, and Nginx, illustrated with architecture and process diagrams.
This tutorial shows how to install the JsonPath library, retrieve JSON data from an API, and use both traditional indexing and powerful JsonPath expressions—including filters, wildcards, and slicing—to efficiently extract nested values for automated testing.
This guide explains how IntelliJ IDEA's built‑in REST Client provides all Postman features plus environment handling, shared request scripts, response assertions and token management, making it a more efficient tool for everyday API debugging and testing.
This article explains how RESTful API design reduces testing complexity by using unique URIs, standard HTTP methods, and JSON data exchange, and shows how test engineers can extend their frameworks with serialization support and added PUT/DELETE operations.
The article explains the shift from unit testing to interface automation, outlines its advantages such as early testing and system robustness, details essential implementation techniques like protocol simulation, data‑driven testing, keyword‑driven execution, and provides practical steps for teams to begin API testing effectively.
This article describes how to efficiently test billions of API parameter combinations by replacing naive nested loops with a queue‑based concurrent approach, dynamically controlling QPS, and addressing memory‑pressure issues using thread‑safe data structures.
This article shares the author’s personal transition from manual functional testing to automation testing, outlines a detailed learning roadmap covering Python, Selenium, Robot Framework, API testing, CI/CD, and provides curated resources and QR‑code links for aspiring automation engineers.
This comprehensive guide walks you through installing Postman, navigating its interface, sending your first request, and mastering essential and advanced features such as collections, batch execution, logging, assertions, variables, pre‑request scripts, request chaining, and extracting nested JSON values for effective API testing.
The article explains why unit testing matters, why managers should focus on higher‑level API tests rather than method‑by‑method coverage, and provides practical checkpoints for transaction, query, and compensation interfaces to help technical leaders promote a results‑oriented testing culture without overwhelming developers.
In this firsthand account, a technical leader describes how adopting the Apifox API testing and mock tool transformed their small company's development workflow, cutting interface testing, integration, and client support times dramatically while improving code quality and overall team efficiency.
This comprehensive guide walks you through Postman's installation, interface navigation, basic request types, response parsing, collection management, batch execution, logging, assertions, variables, pre‑request scripts, request chaining, and common value extraction, providing practical steps and code examples for effective API testing.
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 explains the distinction between functional and interface test cases, presents common business testing scenarios, and details a step‑by‑step process for designing, binding, executing, and evaluating automated test cases to improve test coverage and efficiency.
This article outlines the background, testing strategy, and practical implementation of automated API testing within a large-scale microservice environment, detailing the shift from traditional test pyramids to a honeycomb model, technology choices, test case design, mock servers, platform management, and measures to prevent test suite decay.
This article details Baidu's large‑scale intelligent testing framework, describing how AST‑based unit test generation, automated API test creation, visual UI interaction case synthesis, GUI traversal action set generation, and front‑end assertion automation work together to achieve high‑coverage, low‑cost automated testing across multiple languages and platforms.
This article explains why test data is essential, introduces the Apifox tool as a low‑cost, fast solution for creating both generic and domain‑specific test data, and provides step‑by‑step guidance on using its mock engine, custom rules, batch generation, and automation features to produce reliable testing datasets.
This article examines the challenges of high‑cost test case maintenance and low‑efficiency regression, then details how HTTP and microservice traffic replication, automatic seed case generation, and integrated CI/CD‑driven interface and process automation can dramatically improve testing speed, coverage, and quality.
HTTPie, a popular open‑source CLI HTTP client, lost all its 54k GitHub stars after its maintainer accidentally set the main repository to private, prompting a detailed blog post that explains the mishap, GitHub’s response, and recommendations to prevent similar catastrophic repository errors.
This article explains what HTTP redirects are, why they are used, and how they affect everyday API testing with tools like JMeter and Python's requests library, providing practical examples and guidance on handling redirect behavior.
This article shares a small team's journey from scratch to advanced API testing, detailing how they adopted HttpRunner, upgraded to its pytest‑compatible v3.x, and later integrated Apifox for streamlined documentation, mocking, and automated tests, while highlighting practical setup, hooks, and lessons learned.
This page compiles a curated list of technical articles covering API testing, HTTP client usage, socket programming, Java NIO, and related backend development topics, each linked with its publication date for reference.
The article outlines how intelligent automation—leveraging data‑driven algorithms for automated case generation, dynamic execution, diff‑based result analysis, noise reduction, failure diagnosis, and effectiveness evaluation—addresses API testing challenges such as manual case writing, low efficiency, redundancy, and reliability, ultimately boosting automation ratios, cutting long‑tail tasks, and enhancing test efficiency and quality.
Discover seven powerful Visual Studio Code extensions—GitHub Copilot, Thunder Client, Snipped, Lorem.space, AI Doc Writer, Blockman, and Live Frame—that enhance productivity, streamline API testing, simplify code sharing, generate documentation, and preview web apps, helping developers work faster and more efficiently.
This article describes how a Python‑based diff utility was created to compare old and new API responses during a promotion change‑record migration, detailing its implementation, handling of JSON structures, improvements over the first version, and plans for integration with an API testing platform.
This article introduces HTTPie, a Python‑based command‑line HTTP client that offers a simpler syntax, colored output, JSON support, and cross‑platform installation options, and provides detailed usage examples and tips compared with the traditional cURL tool.
This curated list compiles numerous Chinese-language articles and tutorials covering API and interface testing techniques, including Java HttpClient usage, Spring Boot integration, socket and WebSocket testing, performance measurement, automation frameworks, and related tools, offering developers practical guidance for backend service validation.
This article examines the challenges of traditional API automation testing and presents an intelligent testing approach that combines data and algorithms to optimize the entire API test lifecycle, improving efficiency, maintainability, and quality through automated case generation, execution, analysis, and evaluation.
The article explains how to keep external API contracts unchanged while replacing a data source by importing all microservice endpoints into Postman, writing pre‑request and test scripts, and optionally using a Python Flask service with deepdiff to automatically compare old and new JSON responses.
This guide walks you through using Postman for basic API calls and concurrency testing, setting up ApacheBench on Windows, configuring and running JMeter for performance testing, and implementing custom load simulations in Java with CountDownLatch and Semaphore to understand thread coordination and bottlenecks.
This guide explains how to use Apifox for API automation, covering parameter extraction, external data sources, assertions, environment switching, and batch testing to build maintainable, efficient test suites for backend services.
The article outlines the background, mission, technical choices, and comprehensive features of the Tianyan Quality Platform—an internal testing solution built with Vue and Spring Boot that streamlines API automation, environment management, and DevOps integration across multiple business lines.
This article explains how QA engineers can boost testing efficiency by leveraging interface testing—designing comprehensive API test cases, organizing them into reusable collections, and applying them for requirement verification, data construction, and monitoring—along with developing custom test tools to automate repetitive tasks and improve overall workflow.
This article explains how the Apollo testing framework was redesigned to enable scenario‑based API testing, reduce case creation and maintenance costs, improve quality checks, and support advanced features like traffic replay, ultimately enhancing automation efficiency for large‑scale business services.
This article explains how to use APIPOST to send HTTP requests, configure header, query, and body parameters, switch between form-data, x-www-form-urlencoded, and raw modes, view responses, and efficiently import parameter descriptions, providing a practical guide for backend developers.
This guide walks you through using Postman to automate API testing, covering result validation, batch execution, variable handling, environment management, and dependency resolution with practical JavaScript snippets and step‑by‑step instructions.
This article explains how to build traffic‑ratio models for GET APIs, scale them, and implement a random‑selection framework in Java/Groovy to quantitatively simulate real‑world request loads for both individual endpoints and whole services.
This guide walks through Postman's advanced features—including response assertions, sandbox scripting, UI testing, request methods, cookie handling, authentication, collection organization, variable management, command‑line execution with Newman, and exporting test scripts to Python and Java for data‑driven API testing—providing step‑by‑step examples and screenshots.
This article explains Groovy’s default‑parameter feature, shows how to embed scripts and random values in defaults, demonstrates practical API‑testing examples, and compares the execution cost of using default values versus explicit arguments in a high‑iteration performance test.
This article presents a curated set of 140 software testing interview questions covering topics such as API testing, automation frameworks, performance testing, CI/CD, database operations, Selenium, mobile testing, and related concepts, offering valuable preparation material for candidates.
This guide walks you through creating a JMeter thread group, adding HTTP samplers, configuring listeners, using the JSON Extractor to capture response data, and handling multiple variables, enabling you to chain API calls and validate responses efficiently.
This guide walks through Postman's core features, covering GET, POST, PUT, and DELETE request testing, body options, custom headers, console usage, and the four variable types with their priority hierarchy, providing practical examples and official resource links.
This comprehensive guide walks you through installing Postman, navigating its interface, sending your first request, and mastering basic, convenient, and advanced features such as collections, batch runs, logging, assertions, variables, pre‑request scripts, and request chaining for efficient API testing.
This article reveals powerful yet often overlooked Postman features—including environment variables, global and collection scopes, pre‑request scripts, test scripts, and token automation—demonstrating how to streamline API debugging across multiple environments, automate authentication, and manage variable lifecycles for more efficient development workflows.
This article walks through the design, evolution, and technical details of FunTester—a Java/Groovy‑based open‑source testing framework that unifies functional, automated, and performance testing, explains why existing tools like Postman and JMeter fall short, and outlines its distributed testing service and future roadmap.
This article provides a comprehensive Python tutorial covering how to send GET and POST requests, handle HTTPS verification, perform file uploads and downloads, set request timeouts, use basic and token authentication, manage sessions, generate signatures, and organize a test automation framework with configuration, common utilities, test data, test cases, reporting, and execution scripts.
Learn how to use Postman for practical API testing by walking through three hands‑on examples—searching Baidu, interacting with WeChat’s public API, and triggering alerts on a specialized radio communication system—covering request setup, parameter handling, assertions, and common pitfalls.
This tutorial walks you through preparing, installing JMeter and its WebSocket plugin, configuring a WebSocket sampler, and executing a full end‑to‑end test case, including JSON payload handling, result verification, and common pitfalls for reliable backend service testing.
This article details a comprehensive backend API testing automation framework using tools like pytest, Docker, and Jenkins to generate test cases, manage test data, and produce detailed reports.
This guide explains how to extend the moco API with request‑hit monitoring, demonstrates building a lightweight test service, and shows achieving over 50,000 QPS using FunTester, while detailing the available assertion methods and their practical code examples.
This article presents a comprehensive, automated solution for API robustness testing that extracts baseline cases, generates exhaustive parameter‑level test data, executes them at scale, and analyzes results to identify abnormal responses without manual effort, thereby improving testing efficiency and software quality.
This guide demonstrates how to use Python's requests library for sending GET, POST, and HTTPS requests, handling file uploads and downloads, managing timeouts, performing authentication, session and token handling, generating signatures, and organizing a complete API test automation framework.
