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API Mocking in 2024: Definition, Benefits & Limitations

Updated on Feb 13
3 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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More than half of developers state that they spend most of their time developing APIs and nearly 90% of organizations say their investment in APIs will increase or stay the same over the next 12 months. However, 40% of users state that they have experienced API breakage. Therefore, ensuring reliable API functionality is becoming more critical for developers.

In this article, we’ll explore API mocking, one of the techniques used to ensure that APIs are working as expected, its benefits, and its limitations.

What is API Mocking?

Source: Mockoon

API mocking is simulating actual APIs for testing and development purposes. In this process, the primary goal is to obtain realistic responses to the sent requests and so predict the future behavior of the real API. A mock API enables developers to debug an application before productization by evaluating its performance during complex situations.

What are the benefits of API mocking?

Source: SoapUI

  • As mentioned above, API mocking is useful during the development stage. It helps prepare the API to handle extreme cases and detect potential failures and evaluate whether it works as expected in the functional testing stage.
  •  API mocking is also useful in non-functional testing processes. It is possible to evaluate the performance and response times of APIs in various scenarios and in a shorter time than creating a real back-end through API mocking.
  • By mocking the external dependencies of an API, developers can test the API when actual external dependencies are unavailable.
  • API mocking also allows developers to demonstrate an API to clients before deploying or selling the software

Sponsored

PULSE is an AI-powered API testing tool developed by Testifi. Thanks to PULSE, it is possible to:

  • Prevent the potential effects of late testing through shift-left testing,
  • Shorten the allocated time for the testing processes,
  • Enable comparing the test results with previous results.

Leading companies such as Amazon, Vodafone and BMW use Testifi services. 

How to implement API mocking?

You can implement API mocking using either API mocking tools or API testing tools that offer mocking capabilities. To explore API testing tools, check our sortable/filterable list. However, in addition to selecting the tool, it is vital to decide the type of API mocking suitable for a specific project. Some differences in terms of mocking include:

  • Data differences: API mocking can use static or dynamic data. The degree of dynamism of the data directly affects the realism of a mock API since real APIs produce different outputs for different data. Therefore, it is important to consider the required level of dynamism in data and design a mock scenario accordingly.
  • Server differences: Depending on the intended use and industry, an API mocking can be implemented on the cloud or a local server. A cloud-based API mocking tool makes collaboration easier, but a locally-based tool can be more efficient in a setting where data security is important.

What are the pitfalls of API mocking?

Some challenges of using API mocks include:

  • Limited scalability: Mocking is useful for testing specific functionality with limited API interactions, which means developers need to create a different mock for each behavior they want to test. API virtualization would be a better approach for a more scalable API simulation. 
  • Maintenance efforts: Ensuring that a mock API is not outdated requires effort. Changes to API features would also change the behavior of the mock, so you need to rewrite them to stay up-to-date.

Further reading

You can also be interested in our articles on:

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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