Application testing has changed over time, moving from manual execution to fully automated frameworks. The smallest alteration to the code or User Interface (UI) can cause fragile scripts to affect the entire test execution. As a result, maintenance projects become more expensive and require longer to complete. Then comes self-healing test automation that uses Machine Learning (ML) and AI for software testing, a ground-breaking method that is used to recognise changes in applications and automatically update and fix test scripts in response.
This blog-post explores how AI can enable self-healing automation, which can drastically reduce manual intervention and reorganise the entire testing process.
AI In Software Testing: How Does It Work?
AI makes it possible for computers to “think” and “learn.” AI has the potential to improve testing and Quality Assurance (QA) in a number of ways, including test creation, integration into CI/CD pipelines, in-progress test recording, result analysis, report generation, and everything else in between.
Similar to testing automation systems currently in use, AI may automate repetitive and routine testing jobs and do so much more effectively. For instance, if you give an AI engine the fundamental variables, conditions, and acceptance criteria, it will generate test code for automated tests rather than requiring you to write scripts by hand.
Even though AI is being utilised to some extent, its potential to enhance testing pipelines is still being explored. Consider all of the current capabilities of automated testing technologies. Now consider its limitations—everything that those tools cannot accomplish. AI for software testing would remove those restrictions.
Developers and testers would no longer need to be directly involved manually. AI is capable of doing nearly every work, with the exception of those that call for human intellect, such as developing business logic, strategy, process innovation, and so forth.
What Are Self-Healing Tests?
Self-healing test automation is an intelligent software testing method that prevents test issues caused by changes in the application’s user interface or web components. Programs can recognise web elements in a number of ways, including by using Name, ID, XPath, and CSS elements. Test scripts malfunction when certain characteristics shift or break, which causes the entire test case to collapse.
Self-healing enables teams to use the Agile methodology in a shift-left manner. Not only does it make the testing process efficient, but it also allows for an increase in production and speedier delivery. There are several commercial and open-source technologies in the market that facilitate the automation of self-healing tests through the use of Artificial Intelligence (AI) and Machine Learning (ML).
Self-healing test automation automates test case updates in response to changes in the user interface. This is how it operates:
- Detection: The framework recognizes when an element has changed or is absent.
- Analysis: To find a different matching element, AI systems examine the UI.
- Adaptation: Outdated locators are replaced by the framework, which dynamically updates the test script.
- Validation: To verify accuracy, the updated test is run.
- Learning: By taking lessons from previous patches, the system keeps getting better.
Self-healing test automation guarantees that tests are robust even in changing contexts by automating script maintenance.
AI in Continuous Integration (CI) and Continuous Deployment (CD) Pipelines
Build, test, and deployment procedures are automated by CI/CD pipelines, which form the foundation of contemporary software development. AI is used to improve performance, decrease mistakes, and increase efficiency when integrated into CI/CD pipelines.
How AI Helps With Self-Healing in CI Pipelines?
Artificial Intelligence in CI/CD pipelines indicates that your pipeline is thinking, observing, and learning in addition to just running scripts. What it can accomplish is as follows:
- Predicts issues: AI models are able to predict issues such as faulty testing or infrastructure issues before they result in failures.
- Auto-remediate: The system automatically determines whether to initiate an incident, roll back a deployment, or rerun a task.
- Anomaly detection in real-time: Unusual activity prompts safety measures, and live logs and data are examined instantaneously.
- Learn from errors: Over time, the system becomes more intelligent with each correction.
AI helps in various stages of the CI/CD flow:
- Build stage: Recognise and avoid problematic tasks, or rerun them concurrently.
- During the test phase: AI prioritises the most dangerous portions and only attempts the unsuccessful ones again.
- Deployment Stage: ML monitors deploy in real time and can automatically roll back if something goes wrong.
- Operate stage: AI can restart containers or activate scripts to resolve issues that arise in production.
Benefits of Self-Healing Tests
Traditional automated tests are created using a predetermined specification of the software tool’s components. Therefore, you will need to manually update those tests to reflect the changes whenever those components are changed, which will take time. Self-healing enables your tests to proactively scan the application for modifications and incorporate them, resulting in more robust testing.
This type of self-healing has the following advantages:
- A decrease in failed tests
By updating themselves to match the needs of pertinent scenarios, self-healing tests cut down on the time needed to repair failed tests and prevent delays in the development and distribution of apps.
- Reduced maintenance expenses
It is costly and a waste of time for your team to continuously update the tests. Self-healing keeps your staff concentrated on the important tasks while reducing maintenance costs.
- Prevents flakiness in objects
The QA team has little influence over the flakiness in test design caused by errors like “NoSuchElementException.” This implies that they may find it challenging to assess the true stability of their test scenarios. Self-healing lowers the chance of failure by automatically updating test designs for the relevant app.
- Increased coverage of tests
Your QA team may create a robust and efficient test suite that they can use for many apps and features by implementing the self-healing test automation technique. This results in faster software delivery timelines overall and a better return on investment for your testing budget.
- Integration of AI
You may combine your self-healing system with other AI tools to improve its capacity to fix problems and make it more accurate at anticipating them.
- A faster feedback loop
Your development team will receive updates on code problems much more quickly with self-healing, enabling them to make the required adjustments before the problems worsen.
Real-World Example of Self-Healing Tests
Let’s take an example of an e-commerce platform.
Situation before self-healing automation and implementation:
- Issue: Test failures occur when an e-commerce site’s UI is often changed (for example, by changing the “Buy Now” button to “Purchase”).
Impact: When test scripts malfunction, manual script upgrades are necessary, which raises the cost of test maintenance and delays releases.
Situation after self-healing automation and implementation:
- Self-healing capabilities are integrated into the test automation framework.
- Identifies a test failure since the “Buy Now” button is absent.
- Examined the UI to find the button that has been renamed “Purchase.”
- Automatically updated test scripts without the need for human involvement.
- Maintained execution with little interruption.
Results:
- Test maintenance efforts are reduced by 80%.
- Quicker cycles for testing.
- Increased release velocity while requiring less physical labor.
Challenges and Limitations of Self-Healing Tests
In order to make room for the extraordinary – creativity and strategic thinking that propel innovation – self-healing technology automates the routine. Developers and testers can now spend more time contributing to customer happiness, organization value, and revenue creation than they do on internal regular tasks. This solution known as self-healing automated tests, however, provides a unique set of difficulties.
Technical challenges that need careful architecture and governance might arise when integrating self-healing capabilities with old and worn-out testing frameworks and current CI/CD pipelines. Also, the human element may be more difficult to integrate than the technological one. It is often seen that QA specialists who have spent years mastering conventional testing techniques frequently show reasonable reluctance to adopt AI-native solutions that drastically alter their everyday processes.
Organizations must assess their unique requirements and use tools that support their objectives. Scalability, vendor self-healing support, and simplicity of integration could be some important considerations. Apart from this, the learning curve for new tools and team member training should also be taken into account, with a preference for no-code where appropriate.
Self-healing must also be implemented using a methodical approach to program and change management that is in line with organizational goals:
- Offer Assistance: To speed up acceptance, make sure team members receive sufficient training on the new procedures and technology.
- From Standard to Scale: Establish common standards and methods of operation gradually to implement and regulate procedures at scale.
- Start Small: To evaluate the efficacy of self-healing automation with quick adaptability and learning, start with a pilot project.
- Monitor and Modify: To maintain value focus, monitor the self-healing automation’s performance and make any required modifications.
- Establish Objectives: To reassure and unify stakeholders, formally state the expectations and motives behind the self-healing activities.
Future of Self-Healing Test Automation
With even more advanced and precise adjustments to UI changes, the future of self-healing test automation seems to be quite bright. The growing application of AI to continually close the gap between test automation suites and User Experiences (UX) is one area of development that relies on self-healing.
The future of self-healing in test automation will also be influenced by the rise of AI agents in the technological landscape. It is anticipated that agents will be gradually integrated into certain stages of the test automation lifecycle, creating new opportunities for testing in intricately linked settings.
It is quite evident that organizations will continue to increase the effectiveness and dependability of their software production processes as self-healing test automation develops. You must note that organizations who adopt these innovations early on will be in a better position to provide quality at speed software, giving them a competitive advantage over those that did not.
Self-healing now takes care of your tests’ dependability. However, test execution speed is also as important and this is where cloud-based platforms like LambdaTest can help with this.
Compared to conventional cloud grids, LambdaTest’s HyperExecute, an intelligent test orchestration platform, can execute tests up to 70% quicker. Self-healing capabilities and lightning-fast execution combine to provide the best of both worlds.
With the help of the amazing cloud-based platform HyperExecute, you can quickly run your tests. Compared to other cloud-based test execution grids, HyperExecute is a platform that runs your tests more quickly. Additionally, the platform provides sophisticated tools to manage the testing process and grows as additional tests are added.
With the help of LambdaTest’s HyperExecute, tests can be run on more than 3000+ browsers and browser versions. You will also have a single easy-to-use dashboard view containing terminal and complete test execution logs for all browsers. Consequently, debugging and resolving issues becomes simpler.
Also, the development of self-healing was expedited by Large Language Models (LLMs). Test automation is strengthened by these models which are trained on enormous volumes of data, allowing them to comprehend and anticipate UI changes.
LambdaTest’s KaneAI is a platform built on modern LLM and is a next-generation testing assistant, a native Artificial Intelligence. Using KaneAI, with the help of prompts, testers can perform automated AI tests on web, mobile, and cloud-based apps. AI in testing has never been this easier!
Conclusion
As more and more sophisticated ideas reach the market, software testing has the potential to evolve. Apps are regularly developed and tested in brief cycles using the Agile methodology. Repetitive jobs are being automated, and manual testing is being replaced.
However, test automation has its own set of problems that are being solved with innovative solutions. Self-healing test automation is the way of the future that reduces delivery time and helps avoid potential failed test cases.