Recovery testing: what, why, and how

Software is designed to be highly reliable, but it can still fail due to network issues, hardware malfunctions, and bugs. This is where recovery testing comes in. It’s a type of nonfunctional testing that can tell you if and how well an application can recover from unexpected failures and crashes, including power failures, network failures, and external server issues.

In this post, you’ll learn what recovery testing is, why it’s important, and the various types. You’ll also learn how to implement it and the available tools that can help you.

Recovery testing is a kind of software testing that helps you determine software’s ability to recover from unexpected issues, crashes, and failures.

What is recovery testing with an example?

Recovery testing is a kind of software testing that helps you determine software’s ability to recover from unexpected issues, crashes, and failures. The technique involves intentionally causing problems and simulating different failure scenarios to see if the software can handle unexpected and unforeseen circumstances without losing data or failing completely.

Let’s look at an example. Imagine there’s an online banking application processing a money transfer from one account to the other when the server unexpectedly crashes. To test the application’s ability to recover, a test would involve simulating this scenario (by using tools like Chaos Monkey, for instance) and observing how the app behaves once power is restored and the server is back online.

Why is recovery testing important?

Recovery testing makes sure the system is reliable in the face of events like signal loss, power outage, and network issues and that it can restore normal operations. It’s also important for the following reasons:

• You can identify and fix possible vulnerabilities that can cause downtime or failures before they become a bigger problem and make sure that the system stays operational. This also helps reduce the cost of data loss and downtime.
• It protects data from corruption or loss in case of system failure.
• Removing potential bugs can improve system performance and make sure the system is reliable in case of failure.
• Minimizing data loss and downtime during unforeseen circumstances can maintain better user experiences.
• Helping the system quickly recover from disruptions improves stability.

Types of recovery testing

The main types of recovery testing include the following:

Disaster recovery testing

Disaster recovery testing focuses on an application’s ability to recover from large-scale failures like power outages, cyberattacks, and natural disasters. It usually involves testing backup and restoration processes and data replication.

Environment recovery testing

Environment recovery testing focuses on how well software can recover from changes in environment configurations and dependencies.

Database recovery testing

Database recovery testing evaluates an application’s ability to recover from a malfunctioning or corrupted database. It focuses on testing database backup and data integrity to make sure the database can be restored and that data isn’t lost or corrupted during the process.

Crash recovery testing focuses on how well a system can recover from a sudden crash, such as an application crash or server failure.

Crash recovery testing

Crash recovery testing focuses on how well a system can recover from a sudden crash, such as an application crash or server failure. It evaluates data integrity and determines how well the system performs once it restarts to make sure it can continue working normally without losing data.

Security recovery testing

Security recovery testing focuses on the software’s resilience and ensures that it can recover from security incidents like data breaches, unauthorized access, and cyberattacks. It helps find loopholes in security measures to reduce the impact of security-related events.

Network recovery testing

Network recovery testing involves simulating failures in network connectivity like latency and network outages to test how the system behaves and how well it can recover. It’s particularly important for apps that heavily rely on network connectivity to function.

Load and stress recovery testing

Load and stress recovery testing helps you understand how software works under stress situations or heavy loads. This can help you determine whether it can handle high loads and how long it’ll take to resume normal operations if it can’t handle the load.

How to implement recovery testing

To implement recovery testing, follow these steps:

1. Make a list of possible scenarios where the software could fail, such as data corruption, a network outage, and hardware failure.
2. Define your recovery goals, such as the maximum acceptable downtime. Once you determine these goals, it’ll be easier to evaluate your software’s ability to recover.
3. Now that you know your possible failure scenarios and recovery goals, you can create a test plan. This should include your objectives, the scope, the tools you’ll use, and the testing environment.
4. Create possible test cases for the failure scenarios you identified earlier. Include all the steps to reproduce the failure, the expected outcome, and the criteria you’ll use to determine whether the system passed or failed.
5. Set up your test environment (network configurations, hardware, and software) so that it’s as similar to your production environment as possible. This will ensure that the test results depict your software’s ability to recover in real-world scenarios.
6. Once you have your test cases and test environment set up, run the recovery test cases and see how your software responds.
7. After running all the tests, review the results. Compare them with your recovery goals and identify areas for improvement.
8. Address whatever problems you discover to improve your software, and rerun the tests to verify that the changes have worked.

Recovery testing tools

You’ll need a few tools to perform recovery testing, and you can categorize them in the following way:

Chaos engineering tools

Chaos engineering tools are used to simulate failure. They deliberately introduce faults and disruptions into the system so you can observe how it performs. Some common chaos engineering tools include Chaos Monkey and Gremlin.

Monitoring and observation tools

Monitoring and observations tools like Datadog (for real-time monitoring) and Nagios (for monitoring network and server health) monitor a system’s health, behavior, and performance during recovery testing. They provide insights and collect data that you can use to analyze your system’s ability to recover from disruptions.

Performance testing tools like NeoLoad stress the system by simulating high-load conditions. They can help you test how well your system recovers under stress.

Performance testing tools

Performance testing tools like NeoLoad stress the system by simulating high-load conditions. They can help you test how well your system recovers under stress.

Backup and recovery tools

You also need backup and recovery tools like Veeam so you can back up and restore your data while performing recovery testing.

FAQs

What is data recovery testing?

Data recovery testing is a type of recovery testing that focuses on a system’s ability to restore data after a sudden failure, unplanned disruption, data corruption, or accidental deletion. It ensures that there are dependable data recovery mechanisms in place so that you can recover data after unexpected events.

Is recovery testing performance testing?

Recovery testing is a kind of nonfunctional testing and is a part of performance testing.

Conclusion

Recovery testing is a crucial part of software testing. It ensures that systems can handle and quickly recover from unexpected disruptions, failures, and crashes. By simulating different failure scenarios, recovery testing can help you find possible weaknesses in your software, and by addressing these, you can enhance its reliability. Ultimately, this can help reduce downtime, preserve data integrity, and maintain user trust.

This post was written by Nimra Ahmed. Nimra is a software engineering graduate with a strong interest in Node.js & machine learning. When she’s not working, you’ll find her playing around with no-code tools, swimming, or exploring something new.