Comparing Autonomous Testing to Traditional Methods

Typical modern software development lifecycle testing includes large amounts of automated testing and some manual testing. Due to recent advancements in AI and machine learning, autonomous testing is becoming more popular. In this article, we will compare autonomous testing to traditional testing methods in terms of time to test, coverage, cost, effectiveness, and what challenges each has.

Automated testing

Unit Testing

Unit tests form the cornerstone of all automated software testing. Software developers write unit tests to test individual components of the software. Unit tests cover the smallest testable parts of the software, usually functions or methods, UI or backend. Unit tests are highly effective for regression testing because they run quickly and efficiently. Additionally, when a unit test fails, it is typically easy to diagnose and resolve.

• Time to test: Moderate, Writing unit tests takes time. However, it can be lowered by using gen-AI to generate unit tests.
• Coverage: High, can cover all functions and methods in the software.
• Cost: Low, unit tests are usually easy to write and maintain.
• Effectiveness: High, unit tests are very effective at catching bugs early in the development process.
• Challenge: Writing good unit tests, requires a good understanding of the software and the ability to write testable code.

Api Testing

API tests are used to test the communication between different parts of the software, they typically test the backend and database. API tests cover higher-level functionality than unit tests, and are still relatively easy to write and maintain. However, API tests can be more difficult to reason and resolve than unit tests, as they cover more complex functionality.

• Time to test: Moderate, similar to unit-tests and it can be lowered using gen-AI to generate API tests.
• Coverage: Moderate, can cover all API endpoints in the software, misses the user interface.
• Cost: Moderate, API tests are more complex than unit tests, but still relatively easy to write and maintain.
• Effectiveness: High, as API tests also cover parts which are not covered by unit-tests f.e. database queries.
• Challenge: Covering more complex scenarios than a single endpoint requires orchestrating numerous requests which cause maintainability issues.

Specialized Testing

Integration, performance, and security tests are specialized tests that cover specific aspects of the software. These tests are typically more complex and time-consuming to write and maintain than unit and API tests.

• Time to test: High, specialized tests usually require custom tooling, deep understanding of the software and used technology.
• Coverage: Low, covers only a very specific aspects of the software, such as integration, performance, and security.
• Cost: High, specialized tests require more effort to write and maintain.
• Effectiveness: Low, simulating production-like environment for tests is usually difficult.
• Challenge: Test environments and production environments differ by configurations, data and infrastructure. Specialized tests also require people capable doing them.

End-to-end testing

End-to-end tests are used to test the software as a whole, from the user interface to the backend and database. These tests are typically implemented using a tool like Selenium or Cypress, which can simulate user interactions with the software. End-to-end tests cover the highest level of functionality, but are also the most difficult to write and maintain.

• Time to test: High, writing these tests are slow due to the dynamic nature of applications and amount of interactions required to cover a simple process.
• Coverage: High, can cover all parts of the software, from the user interface to the backend and database.
• Cost: High, dynamic views, changing environments and evolving software cause unintentional maintenance to the tests. AI-assisted tools try to address this issue with self-healing tests with mixed results.
• Effectiveness: Moderate, end-to-end tests are prone to fail for unintentional reasons.
• Challenge: Writing and maintaining end-to-end tests is very time-consuming and requires high amount of maintenance effort especially if the software is in early development phase.

Manual testing

Manual testing is usually the last step in the testing process, and is used to test the software as a whole. Often this step is left to the QA team, or domain experts, who are responsible for ensuring the software meets the requirements. Typically manual testing phase is labeled as acceptance testing, as it is used to determine if the software is ready for release.

• Time to test: High, theoretically low, but in reality people have their schedule full of other work and tickets can wait days for testing.
• Coverage: High, can cover all parts of the software, together, or individually from the user interface to the backend and database.
• Cost: High, manual testing is very time-consuming and requires human intervention. People need training and time to learn the software and testing skills.
• Effectiveness: Moderate, manual testing can be effective when not done too often and people have right mindset for testing.
• Challenge: It depends on persons personal knowledge, it is very difficult to scale well and people get distracted easily, and yet human errors happen.

Autonomous testing

Autonomous testing is a new approach to software testing that uses AI and machine learning to automate the testing process. Autonomous testing agents can work 24/7, and run multiple different testing in parallel. Unlike other testing methods, no test cases are planned before the execution. This makes the approach interesting as the agent can notice issues that were not thought at all during the development process.

• Time to test: Low, "click to run".
• Coverage: Moderate, can cover the software from the user interface to the backend and database, however its ability to test complex process should be evaluated.
• Cost: Low, autonomous testing agents typically work without human intervention, no test cases are written or maintained.
• Effectiveness: High, autonomous testing agents can run multiple different tests in parallel, and learn from the past.
• Challenge: Autonomous testing agents needs to keep the number of false positive issues low to provide relevant feedback for the R&D team.

Are you finding a high number of issues using manual testing? That might be an indication of issues earlier in the software development process. Autonomous testing can help you find these issues earlier and provide relevant feedback for the R&D team.

At AutoExplore, we are committed to helping R&D teams implement autonomous testing as part of their development processes. Ready to transform your process? Contact us for a demo to learn more.