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Balancing quick deployment and ensuring top-notch user experience has become increasingly difficult. While traditional testing methods are reliable, they often lag behind the demands of modern software development cycles.
As a result, autonomous testing has gained increasing popularity in recent years. However, it is important to remember that although autonomous testing seems impressive, due to its complexity, it has yet to be the primary approach in software development. It is like self-driving cars, which exist, but we still can't rely on them entirely to drive us everywhere without a human driver.
Nonetheless, we can explore the notion of autonomous testing, which this blog aims to achieve. We will delve into the basics of autonomous testing, its benefits, and how it can help software developers enhance their testing processes' efficiency, accuracy, and reliability - when approached systematically.
The path to autonomous testing user interfaces
Autonomous testing is a relatively new way of conducting software testing. In contrast to automated testing, which is scripted and does what it is told, autonomous testing adapts to changes in the AUT (application under test) and decides on its own what the best actions to take are.
The primary objective of autonomous testing is to improve the testing process by taking over repetitive tasks. This allows testers to focus on critical aspects of functionality, making testing more comprehensive and effective while reducing the time and resources required for the task.
We presented a proposed approach in our whitepaper, Towards Autonomous Testing: How can we automate test case design, offering a detailed and technical view of our suggested autonomous testing methodology:
- Exploration
This phase involves understanding the application's current state by capturing snapshots and interactions, forming the basis for further analysis.
- Generalization
Generalization looks at patterns across multiple test runs to derive a model of the application's behavior, facilitating a more efficient testing process.
- Optimization
The optimization phase focuses on refining test cases to find the most effective and efficient paths through the application, reducing redundancy and ensuring that critical functionality is thoroughly tested.
- Validation
The final phase ensures the application behaves as expected by validating the effectiveness of the test cases, the overall testing process, and the reproducibility of test cases.
Each phase contributes to developing a comprehensive testing approach.
During this process, data is collected on application states and interactions to build a model that can identify and optimize test cases. This ensures that the application behaves as expected.
Benefits of autonomous testing
Autonomous testing contributes to the "shift-left" testing approach, which involves integrating testing earlier in the development lifecycle. This shift aims to detect and fix defects sooner, improving software quality and reducing costs and time to market.
Autonomous testing has several benefits that can help streamline the testing process and make it more efficient. The main advantages of adopting autonomous testing are as follows.
Reduced testing effort and costs
Autonomous tests can be executed earlier in the development cycle, reducing the time and effort invested in manual testing. This is especially beneficial in complex software systems where manual testing can be time-consuming and error-prone.
Additionally, autonomous testing can be highly beneficial for regression testing, ensuring that previously identified issues do not arise again.
By automating the testing process, companies can reduce the need for manual labor and use costs for personnel more effectively. This can be especially useful in large organizations where testing can be time-consuming and resource-intensive.
Improved testing coverage and focus areas
Autonomous testing effectively evaluates non-functional requirements, freeing up the QA team to focus on functional testing. This approach enables thorough testing of non-functional elements such as performance and security, even for newly added features - which are not covered in automated test cases.
Generation of optimized test cases
Autonomous testing is a powerful tool to create optimized test cases tailored to a project's requirements. This is possible because autonomous testing tools can analyze the AUT and identify areas most likely to fail. In turn, this allows the creation of test cases that focus on these areas, ensuring that the most critical aspects of a project are thoroughly tested.
The future of software testing: Embracing autonomous testing
Autonomous testing presents certain difficulties, such as defining expected behavior, managing the limitations of dynamic analysis, and mitigating the side effects of in-depth data collection. While the journey toward fully autonomous testing is still in its early stages, the potential for future developments, such as identifying new features, visual regressions, and usability issues, offers exciting prospects for the field of software testing.
As we approach a new era in software development and quality assurance, autonomous testing offers a glimpse into a future where testing is not a bottleneck but a powerful engine driving innovation and quality. By embracing these new methodologies, we can expect software that exceeds our expectations and delivers faster and more confidently than ever.
Whitepaper: Towards Autonomous Testing
We recently released a whitepaper on our approach to autonomous testing. This whitepaper is suitable for QA personnel looking to gain a deeper understanding of the topic.
Learn more about our automated GUI testing tool: Squish
If you have any questions or would like to schedule a meeting with one of our experts, please contact us.
