To keep performance insights relevant as architectures evolve with microservices, serverless, and edge computing, it’s crucial to continuously feed real-time performance data into AI models. As these systems grow and change, the AI models need to adapt by constantly updating predictive thresholds.
This way, the system can stay in tune with the latest trends in how your infrastructure is behaving, ensuring your performance insights always reflect the current reality, no matter how much things shift.
Using AI for performance testing definitely comes with its challenges. First off, data quality is crucial, if your data is inaccurate or incomplete, the results can be misleading. Then there’s the issue of model drift, where over time, the AI model might lose accuracy due to changes in the system or environment.
Finally, interpreting probabilistic outputs can be tricky, as AI often deals with probabilities rather than certainties. To tackle these, it’s important to regularly validate the model, use a wide variety of datasets, and involve human reviewers to catch anything the AI might miss.
In Jerome Antonisamy’s session at TestMu 2025, the concept of a “self-healing” performance system was discussed. The big benefit here is that it can automatically spot and fix bottlenecks, leading to less downtime. However, there’s a catch, if the AI misinterprets certain signals, it might mask deeper, more systemic problems.
To make the most of self-healing systems, it’s best to pair them with observability dashboards. This way, humans can oversee the process and step in when needed to ensure nothing important slips through the cracks.
In the world of quality engineering, it’s all about making AI work for you, not replacing you. A great way to do this is by embracing continuous monitoring and feedback loops, which keep you on track and help improve processes over time.
AI-driven testing should be scenario-based, adapting to real-world situations for better accuracy. And most importantly, fostering a shared ownership between development and QA teams ensures AI becomes a valuable partner in enhancing your testing efforts.
Chaos engineering plays a vital role in resilient platform engineering, especially in the age of AI. While AI can simulate potential failures and predict how they might ripple through systems, chaos engineering takes it a step further.
It intentionally introduces random disruptions to test how well your systems hold up under unexpected conditions. By combining these two, we ensure that platforms are not only prepared for the known but also resilient against the unknown, making them stronger and more reliable in the long run.
To predict performance bottlenecks before they even happen, AI looks at historical data, traffic trends, and the relationships between different parts of your system. By feeding this information into AI models, it can spot patterns that might indicate trouble down the road.
Once it identifies potential issues, the AI can send out alerts or even take action, like scaling resources ahead of time, so you’re ready before the problem becomes a real bottleneck. It’s like having an early warning system for your system’s health!
In day-to-day performance engineering, developers can really benefit from AI by using it to spot anomalies in real time, automatically generate testing scenarios, and even trace back to the root causes of performance issues.
Meanwhile, testers play a crucial role in validating these AI-generated predictions and making sure the edge cases are thoroughly tested. This teamwork between developers and testers helps to create a more efficient and proactive approach to performance engineering.
AI helps by spotting potential issues and hotspots in your system before they become real problems. It takes a deep look at your distributed systems, so you can scale proactively, automatically fine-tune performance, and plan capacity based on real data.
This makes managing complex environments a lot easier and ensures your system remains resilient and can handle the load as it grows.
After adopting automated test data management, teams can measure ROI using a few key metrics. Start by looking at how much faster your test cycles are,cycle time reduction is a big win! Also, keep an eye on the defect escape rate: are fewer bugs slipping through?
Then, there’s coverage, better coverage means you’re testing more scenarios without extra effort. Don’t forget infrastructure savings and manual effort reduction, both of which directly impact the bottom line. These metrics help show the value to both your QA team and business leaders.