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Version: 1.0.0

System Introduction

What is Service Visualization?​

The article introduces a brief roadmap for users when using Service Visualization.

Service Visualization is an integrated collaboration platform for API documentation, API debugging, API mocking, and API automated testing, designed to address the issue of data synchronization between different systems by using a set of systems and a set of data. API debugging, API data mocking, and API automated testing can be used directly without the need for redefinition, as long as the API documentation is clearly defined.

After debugging, Service Visualization allows you to use the same tool for both API documentation and API development, helping to ensure that everything strictly adheres to the API documentation, thereby increasing the speed and performance of the entire process.

Challenges in API Management​

Most research and development teams typically use various tools to manage APIs:

  • API documentation management with Swagger
  • API debugging with Postman
  • Mocking API data using tools like faker.js
  • Using JMeter for API automated testing

Maintaining data consistency across different tools is a major challenge and is inefficient. It's not just about the workload; the bigger issue is the inconsistency of data across platforms, which leads to ineffective collaboration and frequent difficulties, making life harder for developers. Common issues include:

  • During debugging, developers have to redefine the API in Postman after defining the documentation in Swagger.
  • When front-end developers mock data, they have to redefine that data using Faker.js and manually write the mocking rules.
  • QA engineers need to redefine it in JMeter.
  • After the front-end and back-end developers have completed development using data from Faker.js and Swagger API documentation, once they finish development and debugging, they may realize:
    • The API has changed during development, and only Swagger was modified, but Faker.js was not updated in time.
    • It can be very difficult to identify issues, such as the API data type being inconsistent with the API documentation during back-end development.
  • Similarly, test cases written in JMeter can also reveal many inconsistencies when running.
  • These inconsistencies will worsen over time.