Unlocking the Power of API Testing: Ensuring Robust, Secure, and Scalable Systems

Introduction

API, short for Application Programming Interface, serves as a framework facilitating communication between various systems. This interaction occurs through a shared contract defining protocols, functionalities, and more. Additionally, APIs play a crucial role in decoupling different systems, thus mitigating the risk of a single point of failure.

For instance, imagine you have n disparate systems interconnected. An API can serve as a decoupled interaction point where clients can communicate with it, allowing the API to relay messages to the systems and return the appropriate data and status.

Over the years, there has been significant development in API implementation across different languages and frameworks. In this blog post, we will explore efficient methods for testing APIs to ensure the development of robust, secure, and scalable systems.

Unit Testing

Unit testing involves assessing individual components of the application independently to ensure they function as expected. These components could range from database CRUD methods to API request validators. It's crucial to test the entire code logic with various scenarios for each component.

The metric used to gauge the extent of unit test coverage across the entire codebase is referred to as code coverage. Numerous methods and tools exist for measuring code coverage, depending on the programming languages and frameworks utilized.

In certain scenarios, there may be dependencies within the systems, making it impossible to test certain components individually. In such cases, all dependencies for that component must be mocked.

Common Tools: Pytest, Junit etc.

Integration Testing

In typical scenarios, an application interacts with various components such as APIs, services like queues, caches, streams, databases, and more. Integration Testing is the methodology employed to assess the operability of all these systems collectively.

During integration testing, the application is tested while all components are deployed. The integration test suite for an API should encompass the following:

• Ensure interaction with all API endpoints using different parameters and request bodies.
• If the API is secured with an Authentication & Authorization component, ensure that the test suite has the necessary access for interaction.
• Since the API might perform CRUD operations in the services using different endpoints, ensure to clean all data once testing is completed as part of the post-processing step.

Common Tools: Pytest, Junit etc., with the deployed applications in synchronized with the test suite.

Load Testing

API load testing involves pushing the API to its limits to understand the threshold limits of the system. There are primarily two approaches to conducting load testing:

1. Sending a large number of requests within a specific time frame.
2. Sending requests that involve high computational resources, such as fetching large amounts of data.

During load testing, it's essential to store metrics like the time duration taken at each stage in the API, including DB calls, aggregations, CPU and memory usage, etc. Analyzing the generated report enables necessary adjustments to be made according to the business requirements for scalability, ensuring the API can handle increased loads effectively.

Common Tools: Locust

Contract Testing

In the era of microservices, applications often interact with multiple APIs to fulfill a single use case. Contract testing is a framework designed to ensure that upstream systems haven't altered the contract or pact as previously defined.

In contract testing, a pact or contract is established between two systems. This pact file is typically stored in a common location accessible by both systems, ideally a cloud storage service. During the continuous integration (CI) process of the upstream system, a step is included to validate this pact. The CI process does not complete until the pact is either updated or validated.

This approach facilitates smooth transitions to newer versions, if required, between two systems and teams. Numerous frameworks offer out-of-the-box solutions for Pact/Contract Testing.

Common Tools: pact-python

Health Checks

An API interacts with various systems such as databases, caches, cloud services, upstream APIs, etc. Implementing a health check mechanism is considered good practice to verify the availability of these systems.

Typically, the health check system is designed to trigger at regular intervals, say every x amount of time. If any issues are detected, automated notification alerts, such as emails or Slack messages, are configured to promptly notify relevant stakeholders.

Common Framework: A docker based module, scheduled to trigger in equal intervals.

Security Testing

Ensuring the security of our systems requires continuous testing throughout the development phase, rather than addressing security issues after deployment in production.

There are three primary types of security testing:

SCA: Software Composition Analysis

During application development, we often utilize open-source packages and dependencies for various use cases. SCA tests assess different security vulnerabilities in these installed dependencies and flag them for updates to newer versions or for complete removal.

Common Tools: Synk, Mend

SAST: Static Application Security Testing

SAST is a testing methodology that analyzes the source code to identify security vulnerabilities applications may be susceptible to. It scans an application before the code is compiled.

Common Tools: Sonarcube, Veracode, Checkmarx

DAST: Dynamic Application Security Testing

DAST takes an outsider's perspective, similar to that of a hacker. It doesn't require access to the source code for testing and is often referred to as black-box testing. DAST identifies vulnerabilities in an application from an end user's point of view.

Common Tools: Tenable

Security testing often gets ignored in the initial phase of the development. It is highly advisable to have tools and frameworks setup for it much before the development to make the applications more secure.

Conclusion

Testing our software is a testament to our fail-safe approach towards development. While there are many other types of testing and frameworks that can be incorporated, it's crucial to recognize that the industry is constantly evolving, as are the ways applications can crash. Therefore, having a robust testing framework provides us with an upper hand in developing fault-tolerant systems.