Software development is at an all-time high and testing teams are under a lot of pressure to deliver products faster, but they also need to make sure that those products are of impeccable quality.
Risk-based testing (RBT) can help with this because it focuses on the most important parts of the software. Traditional methods, however, can be slow and you need to do a lot of work manually.
This is where intelligent automation (IA) can make a difference – it makes RBT more efficient. It helps spot risks, create test cases automatically, and improve coverage to save time and reduce errors.
Today, we’ll go over how exactly IA improves RBT and explore steps to start using it in your testing process.
What Is Risk-Based Testing?
Risk-based testing, or RBT, is a testing strategy that focuses on testing the most important parts of software based on how likely they are to fail and what the potential impact on the business would be.
Nevertheless, there are problems with traditional RBT. Manually finding and prioritizing risky areas can take a very long time and often leads to incomplete testing because of limited resources.
This is further emphasized by the World Quality Report, which notes that automation has been shown to increase testing coverage by an average of 85% (which is a massive difference). This means that more functionalities can be tested without increasing manual effort.
As risks change, testing can be delayed, and it’s hard to get everyone (developers, testers, and business teams) on the same page about priorities. Optimizing RBT is important to keep testing fast, thorough, and efficient, especially as software becomes more complex and release times get shorter.
How Intelligent Automation Improves Risk-Based Testing
Intelligent automation combines AI, machine learning, and automation to make testing faster and smarter. It helps take care of repetitive, complex tasks that would normally take a lot of time and effort.
When you look at this from the standpoint of risk-based testing, it can change the way testing is done by using AI algorithms to automatically pinpoint and prioritize potential risks. It can look at past data and predict where problems are likely to occur, so the testing can be focused on the most critical areas.
One of the major benefits of IA in RBT is that it can generate test cases dynamically. Machine learning models adapt test cases based on real-time risk factors, which means that as the software changes, the tests change along with it. This way, the tests are always up-to-date with the latest risks.
IA also improves overall test coverage because it automatically runs tests and makes sure that no stone is left unturned and high-risk areas are thoroughly tested without any manual input.
Plus, IA provides faster feedback because it connects directly to the development process, so it can track risks in real-time and quickly update test cases as needed.
Research done by Gartner suggests that by the year 2025, businesses that are using AI-driven/automated testing tools will release software updates 30% faster than those not using IA. If someone has shown you a straightforward and legal method to boost your business productivity by 30% (and proved that it works), you’d surely take the deal.
4 Steps to Implement Intelligent Automation in Risk-Based Testing
The implementation process doesn’t have to be complicated, but you’ll need a structured approach to make it work. Below, you’ll find key steps to follow if you want to incorporate IA into your testing process.
- 1. Risk Assessment and Data Collection
The first thing to do is to gather all the necessary data to assess potential risks. This means: looking at historical defect data, understanding business priorities, and collecting user feedback. It’s important that you work with developers and business teams to make sure everyone is on the same page on how to measure risk.
If you need some clarification on how business processes contribute to risk, search the “process mining explained” term on Google, to see how analyzing workflows can uncover things that are actually not efficient and risk areas that should be factored into your testing strategy.
A State of DevOps Report indicates that IT organizations that leverage predictive analytics for risk assessments are able to deploy code changes 50x more frequently, with a 50% lower change failure rate (compared to competitors).
- 2. Select Suitable Automation Tools
Next, you have to choose the right tools for automating your RBT. When you’re evaluating them, consider the requirements of your system, how well the tools work with your current tech stack, and how easy they are to integrate.
You’ll also want to decide if you want to go with open-source tools or commercial options, depending on how high your budget is and what your needs are.
- 3. Integrate AI and Machine Learning Models
Once you have your tools ready to go, the next step is to integrate AI and machine learning models. These models can predict risk levels and automatically adjust test cases if that’s needed.
It’s very important to train the models using both historical data and real-time information, so they can learn from past issues while adapting to current conditions.
- 4. Continuous Monitoring and Optimization
Finally, set up a system for continuous monitoring and optimization. This involves creating feedback loops to constantly improve your test cases and risk assessments.
Automated dashboards can be a great way to track risks in real-time and generate reports that help you stay on top of any changes. This ongoing refinement will make sure that your testing stays efficient and effective over time.
Conclusion
To sum it up – intelligent test automation can help make risk-based testing faster, more efficient, and better at handling changing risks.
If you use IA, your testing will keep up with the pace of development and you’ll end up working smarter, not harder.