Why You Should Be Using AI in Performance Testing

It is safe to say that artificial intelligence (AI) has shaken things up in the software testing industry. With this hotly discussed technology, it is causing many testing teams to think about their testing strategy differently. 

Many are wondering how best to thoughtfully utilize this powerful tool to better improve their testing — and when it comes to using AI in performance testing, there are many things to consider. 

In this blog, we will discuss what AI in performance testing means, common performance testing pain points that AI alleviates, and ways AI improves traditional performance testing. 

What is AI in Performance Testing?

AI in performance testing means utilizing machine learning and other AI technologies to automate manual components of testing while also creating more efficient testing. AI improves performance testing by automating complex tasks and dynamically adapting to changes within the test environment.

When testing teams utilize AI in their performance testing efforts, they are able to predict issues rather than react to them, rapidly optimize test scenarios, and auto-heal tests in real-time. 

Key Benefits of AI in Performance Testing

When it comes to AI in performance testing, you should think about it in terms of a helpful testing assistant. What AI does for testers is removes the worst parts of testing and enhances the best parts. Below are several ways AI improves upon traditional performance testing: 

• Cost-Efficiency: Drastically reduce the amount of manual labor required for testing, which allows for resource optimization based on requirements and demand — ultimately reducing cost. 

• Accuracy: AI learns from all your historical data and tests. That means it automatically tailors itself to your system and helps identify testing bottlenecks and predict issues. This considerably reduces human error. 

• Speed: AI is a bit like a supercharged engine; it automates repetitive tasks, which frees up a tester’s time to focus on test creation and design. It also rapidly analyzes data and offers solutions faster than manual testing can. 

• Dynamic Improvement: AI is always learning and adapting. Each test means it consumes more data, and more data means it is constantly optimizing your tests as your software evolves. 

• Scalability: Using AI in performance testing also means it will move as fast as you do. The technology’s flexibility allows for rapid scaling of your systems to accommodate increasingly large and complex applications. AI enables multiple tests at once while adapting to system changes with minimal human intervention. 

Traditional Performance Testing Pain Points

Before AI came on the scene, traditional performance testing was rife with difficulties and frustrations. No matter how skilled the tester, below are some common pain points that teams had to deal with without the help of AI. 

Test Bottlenecks Causing Delays

The process of creating, executing, and analyzing tests is one that could potentially take weeks — particularly for large, complex applications. That time commitment severely reduces an organization’s ability to respond to issues quickly.  

This puts the application in danger of falling behind the competition, and even the company at risk of a damaged reputation if issues aren’t discovered until after release. 

Inconsistency

Change is one of the few things that will remain the same for performance testing. Differing network conditions, user behavior, server loads, and application updates must all be taken into account by the manual tester. This is no easy feat; rapid changes that are not properly accounted for can result in differences between test results and real-world user experiences. 

Scaling

Ideally, performance testing should be able to scale as an application grows larger and more complex. What began as simulating dozens or hundreds of users may expand to thousands or millions of users, and a tester must be able to utilize a robust testing tool that can scale with those efforts. Yet a testing tool with that ability is more than likely quite expensive and difficult to manage. 

This inability to properly account for (and predict) sudden spikes in traffic or heavy user loads can lead to downtime that is exceedingly costly. 

Human Error

To be human is to make mistakes. It just goes with the territory. When a team largely relies on manual testing, it creates opportunities for a variety of different errors within the testing cycle. Creating test scenarios by hand can lead to inconsistent test results, and repetitive tasks can lead to mental lapses that result in inaccurate and unreliable tests. 

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How AI in Performance Testing Makes Your Tests Better

Those pain points do not have to be a mainstay in your testing strategy. When incorporating AI in performance testing, you can directly address the issues that are dragging your testing (and release cycles) down. Here are a few significant ways in which AI makes performance testing better:  

Real-Time Issues Fixes

No need for constant human intervention when AI enables your tests to self-heal on the fly. Because the AI in your performance testing is constantly learning from your system, it can help identify root causes of problems — so you can treat the actual problem and not merely the problem’s symptoms. 

Automatic Data Analysis

With AI, testers no longer have to manually sift through data, find patterns, and come to a conclusion. AI utilizes complex algorithms to analyze large amounts of data more quickly than a human can with minimal errors. That means finding irregularities faster than a human can for optimizing metrics like throughput, response time, and error rates. 

Dynamic Optimization

Because AI is always adapting to your tests, it can identify patterns in large quantities of data faster than a human can. This leads to rapid issue detection, thereby resulting in rapid test optimization for enhanced efficiency, improved UX, and money saved. 

Predictive Performance

One aspect of using AI in performance testing is its ability to predict issues before they occur. Because this technology consumes your historical data, it will be able to foresee where bottlenecks could occur. By proactively identifying a system’s load capacity, for example, you can adjust your tests accordingly to prevent crashes. 

4 Ways AI in Performance Testing Increases ROI

Up to this point, we have extensively covered how AI in performance testing enhances the efficiency of your testing. But what does that mean in terms of your organization’s ROI? Take a look:  

1. Lower Costs: Automating the routine, mundane tasks of manual testing reduces the amount of manual labor required throughout the entire testing cycle. 

2. Faster Releases: AI's automation drastically increases testing velocity, which translates to faster time-to-market. 

3. Cost-Effective Scalability: Traditional performance testing tools that scale as you grow can be very expensive. AI reduces that cost while retaining scaling power. 

4. Resource Optimization: AI reduces wasted resources a team faces by learning your system and identifying the optimal allocation of resources like server power and bandwidth.  

Best Practices for Using AI in Performance Testing

There are a number of test data creation tools on the market, so which one do you choose Below are several test data creation tool attributes to consider before making your selection.   

Adapt Based on Project Needs

Each project that your team takes on is going to be different than the last one. You should use AI to tailor your testing requirements and parameters to best suit your needs on a project-to-project basis. Consistently refreshing your tests based on ever-changing requirements is a great way to ensure your AI is in-step with your goals.