In recent times, application development companies have gravitated toward successful approaches like Rapid Application Development (RAD) and continuous integration and development (CI/CD). To meet these requirements, teams are deploying test automation at every stage of app development.
In fact, as businesses grow more digital, a successful DevOps is not achievable without automation testing. This is where AI-driven test automation can power the next transformation wave. Recent studies have found that 70% of organizations believe AI and machine learning (ML) technologies are useful at:
- Reducing maintenance of test cases
- Focusing testing on high-risk areas
- Identifying the root causes of testing failures.
Ashok Karania of Global Alliances talks about the capabilities of AI technology in providing "insightful knowledge from historical data" and augmenting the "cognitive abilities of human beings." He adds that AI in the software testing domain can "combine the different tools, knowledge, and process efficiencies to bring out meaningful results and augment the quality process."
What Is AI testing?
AI testing is the technique of using Artificial Intelligence and machine learning algorithms to test any software product with automation. In this approach, AI algorithms can "ape" human intelligence, while machine learning enables self-learning without any human intervention.
Effectively, AI in software testing is based on the following foundational elements:
- ML can classify objects and make accurate data-based predictions.
- Neural networks emulate the human brain and the way it makes associations.
To that end, AI testing can contribute to specific activities in software testing, including:
- Understanding “human” actions to interact with the application under test.
- Classifying the outcome of various testing activities as possible defects or flaws.
- Measuring how likely any outcome can be a defect.
- Associating events with outcomes.
Why are more development companies adopting AI and ML in their testing process? In essence, AI and ML algorithms can extract useful patterns from available data to improve decision-making. As a result, based on these data patterns, AI tools can derive accurate predictions that prove useful in application testing.
SUGGESTED READ - Taking your Test Automation to the next level with AI and ML
Benefits of AI in Test Automation
AI technology is effective in the domain of test automation and overcomes some of its common challenges, like inadequate test coverage and high maintenance costs. For example, AI-driven test automation can augment the testing team by providing them with a fully (or partially) script-free testing environment.
For each new test automation project, QA testers spend considerable time writing new test cases (despite the existence of reusable code and components). AI can effectively reduce the "manual" time spent on repetitive tasks.
Here are some of the other benefits of AI-based software testing:
- Using image and pattern recognition, AI testing can perform visual testing on applications, thus detecting visual bugs. With functionality testing, AI testing tools ensure that all visual elements are functioning as expected.
- While automation testing eliminates “human” errors, AI elevates test automation by managing repetitive tasks and improving test accuracy. AI technology eliminates any “last minute” chances of any errors in the testing process.
- AI testing improves the test coverage by automatically testing all file contents, database tables, memory drives, and application data.
- AI testing supports continuous testing, thus enabling faster product releases and time-to-market.
- The use of AI in DevOps testing can significantly reduce both time and effort spent in functional and regression testing. For instance, regression tests execute each time there is any change in the application code. For organizations moving to the DevOps model, AI can facilitate daily (or even hourly) testing.
As more organizations adopt Agile and DevOps practices, AI-powered testing is perfectly designed to provide high-quality products.
What are the various methods used in AI testing? Let's discuss that next.
AI-Based Software Testing Methods
Organizations can utilize a variety of methods to implement AI in test automation. Here are some of the popular ones:
1. Regression Test Automation
The traditional mode of regression testing consumes both time and effort. On the contrary, AI-powered regression testing automatically executes each time there is any code modification or addition. With the right set of test cases, the regression test automation method aims to reduce the time spent on regression testing of any application.
2. Self-Healing Test Automation
Test automation scripts often break or fail due to any object or property change. But, AI-enabled self-healing test automation enables the automatic healing of broken test scripts. This method ensures acceleration of test automation with little (or no) manual intervention.
3. Defect Analysis and Prediction
The AI-based defect analysis method combines machine learning and natural language processing to detect application defects accurately. With AI-powered defect detection, test automation solutions can identify the root cause of the defects. This method is helpful for accelerating product development and producing high-quality products.
Ready to Get Started?
Let our team experts walk you through how ACCELQ can assist you in achieving a true continuous testing automation
Why is ACCELQ the best platform for AI testing?
As the pace of software development keeps increasing, we are witnessing the emergence of test automation tools that can support faster product releases. Technology companies are looking at deep learning technologies like AI and ML as the "future of software testing." To that end, AI-based testing provides many benefits, like developers spending lesser time writing test cases.
At ACCELQ, we have delivered the best AI-powered codeless test automation platform on the cloud. Among the key identifiers, ACCELQ's test automation platform enhances functional virtualization and reconciliation. Along with the automatic generation of test cases, you can also maintain your test automation at lower costs. All in all, with our AI-powered tools, you can enhance your test automation to work seamlessly with business logic.
Do you want to adopt a novel approach to your software testing practices? We can help you. Share your business requirements and request a product demo today!
Related Posts
How To Automate Usability Testing in a CI/CD Process?
How To Automate Usability Testing in a CI/CD Process?
Best Practices for Migrating Automated Tests to ACCELQ
