Imagine the frustration when your carefully designed app blows up under peak loads, and you’re left confused and thinking where it all went wrong.
You see, performance bottlenecks might go unnoticed if you don’t test your app under real-world conditions. Traditional testing methods often depend on fixed user loads and scenarios. Hence, any unexpected behavior can potentially cause a crash.
AI can simulate high traffic and erratic usage patterns to predict failures before you move to production.
Research shows that industries most able to adopt AI saw productivity growth measured by revenue per employee rise 27% between 2018–2024—more than three times higher than industries less able to use AI (8.5%).
This blog digs deeper into the whys and hows of performance testing using traditional and automated methods. Let’s get started.
Why Traditional Performance Testing Falls Short
1. Limited scalability and user load simulation
Ai performance testing tools, ideally, should be able to scale as an app grows more complex and receives more traffic. Traditional tools need substantial hardware to simulate user traffic.
Setting up this infrastructure can be expensive and demand regular scaling and upgrades. Traditional tests might struggle to replicate geographically distributed user traffic, which can reduce the accuracy for cloud-based apps.
Cloud-based app components like serverless functions and elastic IP addresses change dynamically, and static tests often aren’t able to scale with this change.
2. Complex microservices architectures
Microservices, unlike monolithic applications, have small, distributed, and interdependent components. While this architectural style is highly resilient, scalable, and independently deployable, it increases the complexities of testing.
Testing all the components of a microservice in the same environment using traditional testing methods might not be realistic, given the dynamic nature of the app and unpredictable user behavior. In fact, a lot of times, the components will not even be available in the same environment at the same time.
It might be extremely challenging to identify performance bottlenecks within the intricate dependencies of microservice-based apps with traditional testing because it treats the app as a single unified block.
Moreover, microservices are designed to scale independently, and traditional testing might not be able to adapt to container orchestration and auto-scaling events.
3. Static anomaly detection
Traditional performance testing relies on static thresholds to detect performance anomalies. You set a limit for a certain metric, like response time > 2 seconds, and if the input goes beyond this limit, it’s flagged as an anomaly.
But here’s the problem:
Dynamic, multicloud architectures have variable workloads, containers spinning up and down, and shifting dependencies between individual components. Container orchestration platforms (like Kubernetes) dynamically scale apps based on traffic and load.
So, a metric flagged as ‘anomaly’ as per traditional testing might actually be normal.
Let’s understand with an example.
Consider a food delivery app. Traditional testing sets a threshold that response time > 2 seconds should be flagged as an anomaly. But on weekends, there might be a surge in traffic, which traditional testing tools will detect as an anomaly.
But dynamic anomaly detection methods use AI for software testing to redefine what’s ‘normal’ by adapting to traffic patterns. It knows that weekend surges are expected and doesn’t trigger false positives.
4. Slower feedback loops
Traditional testing requires testers to painstakingly configure test environments by manually provisioning servers and scripting test scenarios. Performance testing, in many cases, is scheduled near the end of the development cycle, when features are fully functional.
This means you wait for feedback from your users, and then work on fixing them, which is both time-consuming and costlier.
AI Techniques Transforming Performance Testing
1. Automated test generation
Natural Language Understanding (NLU) can reduce your test case generation by allowing you to define test scenarios using plain text. AI models use NLU to read and interpret feature specs and user journeys to extract intended behavior.
This means you don’t have to parse requirements manually. You can even generate test cases by stating input parameters inside spreadsheets, JSON files, or workflow diagrams, and upload the doc into a testing tool.
For example, CoTester uses context-aware AI agents to transform JIRA stories or product specs into complete test cases within minutes, while adapting as the application evolves.
AI will automatically recognize predefined fields like conditions, actions, and expected results, and turn them into test scenarios.
If you want to test cases, you can deploy reinforcement learning into your design stage, and AI will recognize your edit patterns, like tweaking assertions for response time, and improve its outputs to generate more context-aware test cases in future cycles.
2. Scalable load simulation
Load simulation is more than just triggering a large number of synthetic users and testing how your app handles it. It’s critical to ensure performance under realistic usage patterns.
User interactions are messy and unpredictable. They browse based on moods, trends, and seasons. They switch between devices and connect under volatile network conditions. Different time zones can trigger simultaneous usage spikes.
AI doesn’t follow rigid test scripts. It adapts to real user behavior and conditions, and detects patterns like:
- Latency spikes
- Where users drop off
- Features that users engage with the most
- Variations in response time under different network conditions and devices Moreover, AI can dynamically ramp up/down loads beyond defined thresholds to identify breaking points.
3. Predictive analytics
Predictive analytics uses Machine Learning (ML) techniques and statistical algorithms to study historical data and predict future events based on that. It can analyze vast datasets and detect anomalies in metrics like response time, error rates, and throughput.
These analytics help you find potential bottlenecks in your app’s performance.
Predictive analytics techniques include:
A. Neural networks
A neural network is an ML model that makes decisions in a similar manner to the human brain. It’s made up of layers of interconnected nodes, where each node is associated with a weight and threshold.
The input layer takes raw data, like CPU usage, memory load, or response time, and sends it to the next layers. The hidden layers act as decision makers and highlight important patterns like CPU usage more than a certain limit leads to slow response time.
B. Linear and logistic regression
Linear regression identifies correlations between two variables, like performance metrics and system load. It can predict how high user traffic results in increased response time.
Logistic regression is used for classification (yes/no) and predicting the probability of an event occurring based on input. For example, at 10000 users, there’s an 85% chance the app will crash.
C. Decision trees and K-means clustering
Decision trees split performance data into branches based on conditions, to trace why the app might crash.
K-means clustering groups the same performance data into clusters to detect hidden patterns and anomalies. Users can be clustered based on their interaction patterns, like frequency of login, time spent, or actions taken.
Combine Human Oversight with AI for Best Results
While AI can automate many aspects of performance testing, human oversight remains invaluable. It’s important to strike a balance between AI and human testers.
Create a collaborative environment where AI helps in debugging code, identifying errors, and suggesting fixes, while human testers can focus on understanding user requirements and designing complex testing strategies to deliver robust apps.
This saves human testers from spending time on repetitive tasks that can be easily automated, and improves productivity.
This blog is originally published at Testgrid
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