How Testing Chaos helps build and send a formidable software
Is it possible to develop and spread software that never faced any problems? Probably not. But it is possible to minimize the impact of these problems by designing resilience in software with assistance testing chaos or technical chaos.
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"The principles of Chaos Engineering" define practice as "experimental discipline on distributed systems to build trust in system capacity to withstand volatile conditions in production."
It involves the introduction of intentional failures into the software system to measure the ability of the system to overcome and evaluate the impact of failure on the availability and durability of the system.
The concept of Chaos engineering was introduced and developed by Netflix to test their IT infrastructure resilience to ensure a smooth experience for their customers. They call it 'Chaos Monkey'.
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Antonio García Martínez, the author of the book 'Chaos Monkeys' explains the concept as -
"Imagine a monkey entering the 'data center', 'agriculture' server that organizes all important functions of our online activities. Monkey randomly tore the cable, destroying the device and returning everything that passes the hand [IE throwing dirt]. The challenge for IT managers is with Designing the information system that is responsible for so that it can work even though these monkeys, which are never known when they arrive and what they will destroy. "
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The concept may appear to be contrary to intuition, but it is a brilliant tool to prepare your software on potential problems. Instead of waiting for an error occurred because it implements improvements, test engineering tests or chaos taking a proactive approach.
By practicing testing chaos, organizations have further control of errors introduced. With this benefit, they can quickly identify hidden defects, vulnerabilities, and other problems, which may never present themselves during the traditional testing process. Having a critical error lying around may prove fatal in the long run.
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Impact of software failure
History has been a witness to many incidents where the smallest bug in software causes disaster results.
Recently, Heathrow London Airport was hit by technical problems, an impact of more than hundreds of flights. And, two incidents of Max Boeing 737 were unforgettable.
A few weeks ago, the zoom of video conference applications went down for hours, which disrupted the meeting and class around the United States.
Cyberattack recently at the University of Brno Hospital in the Czech Republic caused a computer shutdown directly in the middle of the Coronavirus outbreak.
These are just a few examples of the ocean incidents that occur every year due to unresolved software disorders and disabilities.
This failure causes the organization to be talks about the city for all the wrong reasons. Business and their income is not only affected, but their reputation obtained hard must also face a heavy blow.
Means the time for recovery (mttr) vs means time for failure (MTTF)
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Traditionally, the organization is proud for a long time that they have without blackouts or technical problems. This means that they rely on the average time for failure (MTTF), which is the average time in which the system operates before it fails.
However, the irony is that customers will not remember all the time the software system works perfectly, but will never forget it once it does not.
Therefore, the high time that the organization shifts their focus from the average time for failure means time for recovery.
The average time for recovery is the average time that the software system is needed to recover from failure. Now, the aim for global organizations must minimize the average time for recovery in such a way that customers don't even pay attention when a problem occurs. And this can be possible with help testing chaos.
Testing and DevOps
Let's get one thing that is clear - to inject the systemic error into the software system does not mean it causes an impact on the final customer. Any experiment with the introduction of intentional chaos must remain behind the curtain of the application that works well for end users.
This cannot be done with software development and inheritance methodology. Chaos Engineering completes well only in DEVOPS settings where integral automation with the entire process of testing and software development, monitoring loops and continuous feedback is determined, and there is scope for sustainable improvement.
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When an error is injected into a system, some bugs are found and the vulnerability is identified. With the help of DEVOPS practices, this defect can be resolved in real time and automation can be deployed for future events.
Testing of chaos, combined with DEVOPS, is the best way to develop and provide software that is very tolerant and tough.
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