Why do testers perform chaos engineering?

/ 12th December, 2019 / Best Practices
Why do testers perform chaos engineering?

Chaos engineering is becoming an inevitable part of any software testing process. The newest technologies and loaded applications require modern approaches to the QA process. If you are not familiar with chaos engineering, this post is right for you. What are the basic concepts of this type of software testing? Why do you need to use it in your development process? Here you will find the answers to all these questions, and much more. 

Chaos engineering: the basic concepts 

First of all, it is important to clarify the main idea of this type of application testing. Chaos engineering means finding failures and bugs before they become outages and identifying vulnerabilities in systems in the production environment. The most effective way to implement chaos testing to your development is to examine how your system behaves in stress conditions. In other words, this type of software testing is designed to compare what is supposed to happen under stress conditions to the reality of what really happens. You intentionally break up the standard approach to using the system to discover how it will respond.

You can draw the diagrams and make assumptions about your system’s crashes for dozens of times. However, you will never know for sure how it will respond to serious issues or even shutdown. Chaos engineering is about making top-notch and various experiments aimed to find the real weaknesses in your system. For example, how will your app respond in case it will get a hundred logins from the same IP address? Or what will happen in case of network failures during the registration process? What will happen to the user’s data in case the system crashes? Chaos testing is aimed to get the answers and find the most sensitive and weak points in your system. 

Why do you need to implement chaos engineering? 

  • Boost the flexibility of your system
  • Find the weak points and make it strong 
  • Discover hidden threats 
  • Eliminate the risks of critical errors 

To put it short, chaos engineering can help you to make your platform more reliable and stable, as well as avoid facing different accidents. From this viewpoint, it looks similar to testing. So, what’s the difference? 

The main difference between testing and chaos engineering 

The classical QA process is about creating test cases with certain inputs and outputs that help to investigate certain system’s behaviors. In most cases, testing discovers bugs and crashes on the exact stages of your project. It usually doesn’t provide you with any knowledge about the behavior of your system if something goes totally wrong. 

Unlike testing, chaos engineering will help you get new data about the software’s performance using accurate and precise experiments. When using various uncommon combinations, you get an opportunity to discover the new system’s conditions. This type of engineering offers countless options to test the system from different sides. To put it short, it is about bringing intentional chaos to your software. The main question is whether your app will stand it or crash? 

How to perform chaos engineering? 

As a rule, chaos engineering requires two teams for its implementation. The first team (or QA professional) is responsible for making various experiments. The other team should be ready to deal with and fix the results of these experiments. Chaos engineering is usually performed in a few steps:

  1. Fix and describe the normal behavior of your platform or app 
  2. Make predictions on how the system responds in case something goes wrong
  3. Make experiments using various variables and check the behavior of your system under stress conditions, including:
  • Server failure
  • Network errors
  • Memory issues
  • Dependency failures
  1. Discover and describe the real results of chaos engineering compared to expected ones. 

In case the team finds the system’s weaknesses, the result of the process is considered to be successful. Otherwise, you will need to make further experiments, expanding the boundaries. After you’ve successfully found the weak point in the system, the second team will need to fix an issue. 

Note: it is important to start with smaller experiments to avoid having a total crash of the system. Start small, go far. 

To put it short, the failures found in your system might appear to be the new ways to make your software more stable and reliable. Understanding the key points of chaos testing will help you to discover critical errors in your application and make it more resistant to an unstable environment. With this type of engineering, you become more confident with your system’s features and capabilities. 

Chaos engineering and crowd testing: Why do you need both? 

Chaos engineering is one of the vital steps in any QA process in the production environment. This type of testing requires using various tools for process automation and can perform a major part of the tester’s job without any human assistance. But how can you be sure there are no bugs and crashes outside this predicted test script?
Thus, a QA process should also involve real people with real devices, who can test the software for vulnerability and crashes using their unique skills and knowledge, and all under real-world environments.
After all, what is more, “stress conditions” than real people, using it as real users, trying to “break the system”?  

The truth is that the predesigned scripts can’t play unexpected scenarios and find weaknesses in the whole software environment. However, the ‘Crowd Testing’ model, using real people, might be able to find weak points and bugs even in the least expected places of the product. Moreover, QA experts can also make smart predictions of the product’s performance and offer unique solutions for making it more stable and effective.


To find out more about how Crowd Testing can be helpful in performing chaos engineering please contact the experienced professionals from our team.

Don’t hesitate to contact us at sales@ubertesters.com.

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