The Increasing Need for AI-driven Test Automation in Continuous Testing
Today’s ever-evolving quality engineering landscape demands businesses to focus on the quality-first aspect to drive positive customer experiences. Naturally, companies are shedding more money to achieve quality while minimizing other costs (even marketing). The ROI is ultimately a flawless software product delivered before deadlines.
AI and Test Automation are the future of Software testing; yes, we’ve heard the cuckoo phrases on repeat.
But what does it mean for organizations looking to scale their software testing efforts?
First, quality assurance is no joke; any developer would want his code to be looked into by a human mind which goes beyond the reasoning of binary right and wrong.
So why is test automation proliferating at a rate at which it has put many manual testers into a frenzy?
The answer lies in AI-driven algorithms and test automation processes that mimic human intelligence and way of responding. A study in 2020 by Practitest revealed that a whopping 78% of organizations use test automation for functional testing and regression testing. QA teams are primarily speeding up the iterative tests involving voluminous test data.
Contrary to popular notions, manual testers wholeheartedly embrace AI in software testing because it allows them the freedom to investigate other important issues pertaining to functionality, security, and performance. Special attention should be paid to usability, involving interfaces and user experience, both of which are directly connected to customer experience.
So, what makes AI-driven test automation a prerequisite to continuous testing?
Continuous testing is a way of establishing a safety net that protects the user’s experience in a highly rapid development process.
It helps avoid software failure headlines and assesses the business risk coverage related to the accelerated development changes. It also helps in:
● Providing quick insights if a release is risky to proceed with.
● Executing the right test suites at the right point in the delivery pipeline, thus avoiding bottlenecks. Also, eliminating redundant test suites.
● Stressing the need for a stable test environment and the availability of valid test data. This helps in reducing downtime.
● Seamlessly integrating software testing into the development process as a part of the DevOps toolchain.
● Delivering feedback (with actionable advice).
● Detecting and understanding those changes to the application that impact a valuable, user-friendly functionality.
● Embracing the modern-day software testing approach of Shift left, i.e., working on problem prevention rather than problem detection and subsequent fixing.
● Assessing the end-user experience using end-to-end tests that check the dependencies of functionality between the front end and back end.
Why is test automation necessary for continuous testing?
It goes without saying that manual testing is slow and prone to accidental mistakes. The approach of ‘Automate everywhere you can’ may seem extremely obvious. It is still a fundamental principle worth stating.
Test automation is not just for software development or for quality assurance. As a part of the larger DevOps chain, it extends to the build and ultimate deployment.
Why you may ask?
The reason is simple: the focus of a clean, continuous testing process goes beyond just automated testing. It pays more attention to minimizing problems and maximizing speed.
Continuous testing is a long-term strategy for providing consistently reliable and accurate results. AI-driven test automation helps this strategy to run efficiently, thus leading to a seamless delivery pipeline. It is equipped with plans for the following:
- Choosing the right test cases for automation.
- Developing automation frameworks that are easy to maintain.
- Creating a robust schedule of all the automated tests that are to run along the different points of the delivery pipeline.
3 challenges of continuous testing that can be countered with AI-driven test automation:
Developing and implementing a test automation strategy for continuous testing is challenging. But there is light at the end of this tunnel.
Models such as the test automation pyramid and the core test practice of test optimization can help overcome these challenges. Let us understand these in-depth:
1) Increased complexity and the lack of faster feedback loops
When your product is in to the continuous development methodology, its scope for improvement and enhancement grows too. With every added functionality, a need for subsequent software testing arises, which ultimately increases the number of tests.
The volume of executing so many tests can take a toll on manual testers. Speed and precision are the names of the game, including a quick feedback loop. If lesser tests are executed to make up for quick feedback, it defeats the purpose of continuous testing and leads to a shabby compromise on the quality of the product. Thus, your testing efforts go in vain.
The solution:
AI-driven test automation acts as a catalyst to faster feedback loops and brings the required accuracy into the regression testing environment.
Test orchestration is a great way to combat the challenge of collaboration in a continuous testing team and get instant feedback. Test orchestration goes one step ahead of test automation.
While test automation helps you bring faster validation for the multiple reiterative phases in your software product development, test orchestration schedules a series of automated tests in a predefined fashion. This helps with better test optimization and control.
2) Scalability issues:
Manual testing is not suitable for scaling purposes due to its inability to handle or support extensive test sessions that run simultaneously. Also, slow test execution becomes a painful bottleneck in continuous testing.
Add to it the woes of enormous amounts of test data that is generated.
Often, quality engineers tend to overlook these scalability issues initially because they aren’t really noticeable then. They are only evident once the system gets loaded with a high volume of test data. At that stage, it is hard to switch to another tool/framework.
The solution:
AI-driven test automation helps in creating new tests and subsequently adding them to your new test suite.
It increases the test coverage so that new features are tested appropriately to ensure a quality application. Manual testing usually overlooks lengthy and regressive tests because of their complexity.
Using automated testing can help overcome this oversight by allowing QA teams to write and execute in-depth tests with complex use cases.
When we talk about scaling, let’s not forget the other aspect- Simulation of real-world test conditions. With manual testing, it is difficult to create realistic scenarios to understand a software product’s behavior under various real-world test environments.
But with AI & ML-driven automation algorithms, it is easily possible to simulate the user interaction of multiple users under various test scenarios. This helps developers in gaining a deeper insight into product behavior. For effective test data management, a centralized repository is set up to store and retrieve the resultant data.
3) Lack of testing infrastructure and resources:
Often, organizations choose to embrace continuous testing without thinking about the costs and initial investments associated with it. Additional test environments, advanced tools, and the hassle of hiring experienced automation QA testers can scare many organizations off the path of continuous testing.
Moreover, the lack of testability support in products that were never developed, keeping automated testing as a requisite, poses an additional challenge in what is already a complex testing environment.
A half-baked continuous testing setup does more harm than good. Round-the-clock maintenance of these test environments and the use of specialized tools to fuel the swift feedback loops are other requirements for a continuous testing model.
The solution:
Setting up the testing infrastructure for AI-enabled has a great ROI in the longer run.
It saves you a lot of time, money, and effort, by allowing you to reuse tests for other use cases and easily associate another project to the defined automated test suite.
This means that you can replicate the knowledge of setting up an automated test suite and its related tools easily into a new project. AI in software testing is a great weapon to counter the challenge of repetitive tasks, which can now be handled quickly and efficiently.
Key takeaways:
Organizations looking to seamlessly integrate into the CI/CD ecosystem, rely on continuous testing to scale their speed of delivery and release quality products in the market. DevOps test automation is swiftly becoming the focal point of continuous testing, and the larger CI/CD (continuous integration continuous deployment) pipeline.
The AI-powered continuous testing model is a feasible solution to tackle the quality assurance challenges that pose a risk to innovative upcoming software solutions.
However, an additional hiccup that most of them face, is the need for experienced software testing professionals, and more importantly automation engineers who understand the depths and complexities of an automation environment.
Choosing the right testing partner to enforce continuous testing can be tricky, but it is important as a strong QA team is essential for continuous testing, and its absence is detrimental to the project delivery timeframe
