What Is Intelligent Test Automation & Analytics?
Consider this: The automation testing market is poised for remarkable growth. It is projected to grow at a Compound Annual Growth Rate (CAGR) of 19.2% from USD 20.7 billion in 2021 to a staggering USD 49.9 billion by 2026 (source: dogq.io). These numbers are not merely statistics; they represent a fundamental shift in how businesses approach software quality assurance. Companies are combining their testing efforts with AI and ML to produce intelligent test automation solutions where agility and precision are paramount.
But what exactly does this mean for your organization? How can you utilize the potential of intelligent test automation and analytics to stay ahead in this dynamic landscape? Let’s have a look.
Table Of Contents
- 1 What Is Intelligent Test Automation (ITA)?
- 2 Intelligent Test Automation: The Current Landscape
- 3 Why Teams Adopt Intelligent Test Automation?
- 4 Common Challenges of Intelligent Test Automation
- 5 Real-World Examples of Intelligent Test Automation
- 6 Why Choose Testsigma for Intelligent Test Automation?
- 7 Scope of Intelligent Automation
- 8 Final Words
- 9 Frequently Asked Questions
What Is Intelligent Test Automation (ITA)?
Intelligent test automation is the process of including Artificial intelligence (AI) and Machine learning (ML) in test automation. It goes beyond the traditional automated test methods. It makes use of the two most smart (read, trained) technologies of today to enhance the efficiency, accuracy, and adaptability of the testing process.
ITA requires a model-based approach to testing, in which the application is defined in business or functional terms. The AI model running your test automation then identifies these components and tests them automatically. You don’t need to specifically state the tests that need to be run. Rather, the AI model creates and executes the test cases when it becomes aware of the components.
Intelligent Test Automation: The Current Landscape
If you spend enough time browsing the internet, you will come across multiple real-life examples of AI already influencing the testing processes in organizations. Yet there is limited possibility AI actually brings to the table. Let’s look at the current scenario:
Scriptless or No-code Automation
When you automate your tests, you need test scripts. But AI is changing all that: you don’t need to create complex scripts. No code or scriptless test automation for web applications and mobile is popular among testers as AI helps them to build and run tests in plain English. This requires less or no technical expertise and allows business users to work with automation.
As of 2023, the codeless testing market is valued at USD 2 billion.
A scriptless approach to test automation saves time, produces accurate and smart results, and reduces the software development life cycle span.
Self-healing tests are exactly what they sound like; they are tests that can self-heal without any manual intervention. They may sound like intelligent test cases, but rather, they rely on intelligent tools to maintain themselves. Automated tests do not detect and recover from failures themselves; tools like Testsigma enable self-healing for such tests.
Testsigma comes equipped with the capabilities of AI to detect abnormalities in the existing code. It automatically fixes the changes made to the source code and prevents tests from failing. Here’s a blog for a better understanding of self-healing in Testsigma.
A relatively new segment of AI-based testing is voice-assisted tests. They behave similarly to any other voice assistant but only cater to the testers. The voice instructions are translated to the test steps. So, if you have to validate a registration page, start giving the commands to the voice-assisted tools, and they do the rest. Yet the challenges often outweigh the benefits of this approach:
- Not all tests are simple, like checking a registration page.
- Giving voice commands for complex testing scenarios is definitely a hassle.
- For tests with multiple inputs, you would need to voice them out separately.
- Even a tiny mispronunciation or miscommunication can break the entire test case.
Why Teams Adopt Intelligent Test Automation?
Simply because intelligent test automation is helping them unblock the future of software testing, the benefits are surely tremendous compared to the traditional approach of automated testing.
Speed and Efficiency
Intelligent test automation supercharges testing processes by leveraging AI and machine learning. It accelerates test case creation, execution, and analysis, reducing testing cycles and time-to-market. Automated test scripts adapt to code changes, allowing for continuous testing and quicker feedback loops.
Better and Smarter Decisions
Data is paramount. Even in testing, test data drives the process to success. And when coupled with AI, smart data leads to testers making better and more intelligent decisions.
In a competitive marketplace, rapid software delivery without compromising quality is a game-changer. Organizations that adopt intelligent test automation gain a significant advantage by delivering top-notch products faster and more efficiently.
Imagine a world where you can predict potential defects before they become costly issues. Intelligent automation uses historical data and AI algorithms to forecast areas of vulnerability, enabling proactive testing and risk mitigation. This, in turn, reinforces the importance of using smart data to get actionable insights.
Complex to Easy
AI test automation is simplifying testing for everyone. An excerpt from Grand View Research highlights this very innovatively.
The machine learning & pattern recognition segment held the largest revenue share of over 47% in 2022. Al-enabled testing tools integrated with machine learning and pattern recognition technologies are augmenting the development of Self-Optimizing Networks (SON), which allow operators to automatically improve network quality based on traffic statistics by time zone and location……
The NLP segment is witnessing rapid growth owing to the acceptance of novel technology advancements. Increased customer demands for cloud-based technology and advancements in communication infrastructure are accelerating NLP growth.
So, clearly, testing is leaving behind its complicated days and embracing the simplicity new technologies are bringing forth.
Common Challenges of Intelligent Test Automation
The advantages of intelligent testing are plenty. But there are some challenges as well that require your attention:
- If you are just starting, the process can be complex and resource-intensive. It can be challenging to set up the necessary infrastructure and integrate with existing systems.
- Technical expertise needed to create intelligent automation scripts must be high, prompting testers to learn AI and ML language models.
- It can become time and resource-consuming based on the first two points.
- You might have to train some ML models using large and diverse datasets. Obtaining and curating these datasets can be a considerable undertaking.
- Maintaining updates and integrating intelligent automation tools with existing testing frameworks, CI/CD pipelines, or third-party tools can be complex.
- AI-based test automation may produce false positives (detecting issues that don’t exist) or false negatives (missing actual issues).
Real-World Examples of Intelligent Test Automation
We recommend it is best to understand intelligent automation testing through real-life examples. So, we bring some actionable insights to you.
Intelligent Test Authoring
Intelligent test authoring is creating test scripts or test cases for software testing using advanced technologies and automation techniques. It involves the application of artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to automate the test script creation process. Testsigma uses AI and NLPs to power codeless test script creation for the testers. The tool tremendously simplifies test script creation and test case design, all the while providing smart suggestions and recommendations for building test scenarios. The pre-defined NLPs give access to a vast database of test steps you can easily select and use. Honestly, you don’t have to write a complete sentence as well. To top that, testers can handle test data management right within the platform, allowing customization in the test scripts as necessary.
One example you can see in the below image is the list of NLPs Testsigma offers to execute For Loop and Frame action.
For further understanding of how Testsigma makes use of NLPs, read our blog Smart Test Automation using NLPs.
Intelligent Test Maintenance
Intelligent test maintenance refers to the practice of using automation, AI, and ML techniques to efficiently manage and update test scripts and test cases as software applications evolve. And yet again, Testsigma packs some powerful moves here. The tool includes features for intelligent test maintenance; it can identify and interact with web elements dynamically, even when their properties change. The same is automatically adjusted in the test scripts to align with the modified elements or functionalities.
As your application grows, the test scripts expand accordingly and automatically.
Intelligent Test Automation Analytics
Intelligent test automation analytics uses advanced analytics, data analysis techniques, and AI in software testing and test automation. It involves collecting, processing, and interpreting data generated during the testing process to gain insights, make informed decisions, and optimize testing efforts. In Testsigma, which runs on AI, reporting and analytics are given extra importance. The platform provides detailed reporting and analytics, allowing testers to monitor test execution results and identify issues quickly. After every test run, testers receive a comprehensive report of the pass/fail status, including images and videos of the tested page or product. If your company uses external software to manage the reports, our tools come in handy there as well. Testsigma supports third-party integrations to make the entire reporting and analysis process simple and easy to manage.
Why Choose Testsigma for Intelligent Test Automation?
The answer lies in the tremendous benefits Testsigma packs within just one space. Your testing team can leverage the powerful combination of codeless test automation and advanced AI-driven capabilities to create, execute, and maintain test scripts.
At the core of Testsigma’s brilliance lies its AI-powered test authoring and maintenance. It offers intelligent suggestions, adapts to dynamic changes in application elements, and heals itself when necessary. The result? A significant reduction in the arduous scripting process. Testsigma doesn’t merely automate; it empowers.
And it grows as you grow. The scalability factor that Testsigma brings to the test cases is beyond simply adding more test data input and increasing the size of the scripts. The tool guarantees that your application thrives in diverse digital ecosystems by integrating with CI/CD pipelines. We trust that as a tester looking to move ahead of the curve, Testsigma is truly an intelligent partner to rely on and trust throughout your testing journey.
Scope of Intelligent Automation
The scope of intelligent test automation is vast and continually expanding as organizations seek efficient and effective ways to ensure software quality in today’s rapidly evolving technology landscape. Here are key aspects that define the scope of intelligent test automation:
- Test Script Creation: Intelligent test automation encompasses the creation of test scripts using AI-driven techniques.
- Automated Test Execution: It involves the automated execution of test cases across various environments, browsers, and devices.
- Self-Healing Tests: Intelligent automation can automatically correct test scripts when elements change or fail.
- Predictive Testing: AI-driven predictive analytics can identify potential issues.
- Reporting & Analytics: Intelligent testing runs smart tests to produce results that provide an extensive understanding of the quality of the product and identify areas for improvement.
- Model-based Testing: AI relies on the accurate and comprehensive knowledge of the product’s model to create and run tests.
Even when it might not be true for you currently, intelligent test automation is a reality for many companies. The power of AI supports your testing efforts in a myriad of ways, starting with authoring test creation and maintenance. At the same time, it comes with some limitations.
While AI and ML drive the testing process to become more than just automated test cases, some complex situations ask for the experience of a human tester. No automated process can make it to completion without an individual behind the scenes.
Overall, there is no denying that intelligent testing is the present and future for the testing industry, where QAs can function more efficiently with the right tools and technologies in their hand. Testsigma is at the forefront of supporting such endeavors of every tester with its AI-capable features.
Frequently Asked Questions
What are intelligent test automation examples?
Examples of intelligent testing include self-healing tests, AI-driven test data generation, AI-powered test script generation and execution, and predictive analytics for prioritizing test cases.
What is the difference between automation and intelligent automation?
The difference between automation testing and intelligent automation testing is the use of AI and ML technologies in the latter. While automation merely focuses on the use of tools to automate manual tests, intelligent automation makes use of advanced technologies to generate model-based tests on its own.