What is reliability testing? (With examples)

Why is reliability testing important to software development?

Imagine you’re about to make an online transaction using your banking app. You’ve filled in the details and hit the submit button. The transaction gets stuck in a loading loop and displays an error message. At this point, you’re torn between trying the same app again or banking another way entirely. This is what reliability testing is designed to prevent. In cases like these, a testing tool can simulate high traffic and test the application’s ability to handle multiple transactions at a go.

This kind of testing focuses on predicting and preventing problems throughout a product’s life cycle by assessing how likely they are to happen. Some of the possible outcomes include the following:

1. Customer satisfaction: The less frustrating and buggy an application is, the more satisfied the customer will be.
2. Safety: For critical systems, reliability testing ensures that everything runs smoothly by putting checks in place. This helps prevent potential disasters.
3. Compliance: In regulated industries, having reliable software is essential. For example, in the financial sector, trading platforms must comply with strict regulations to ensure fair and transparent operations. Reliability testing makes sure these platforms can handle high transaction volumes and maintain accurate records.

 

What makes a system reliable?

We can tag systems as reliable when they exhibit the following:

• Robustness: handles errors gracefully without crashing
• Consistency: delivers the same performance across various scenarios
• Availability: is always ready when needed
• Scalability: can grow and handle increasing loads without compromising performance

How is system reliability measured?

1. Mean time between failures (MTBF) is the average time between system breakdowns. The higher the MTBF, the more reliable the system.
2. Mean time to failure (MTTF) measures the average time until the system’s first failure. It’s a key metric for understanding a system’s initial reliability.
3. Failure rate is the frequency of failures over a specific period. Lower failure rates indicate higher reliability.

Now let’s look at the tools that use these metrics while performing reliability testing.

Tools for reliability testing

The tools you use will depend largely on factors like your project needs, your available budget, and your team’s technical experience.

• Tricentis NeoLoad provides a unified testing solution in an integrated environment. Through simplified drag-and-drop modeling, users can recursively craft nuanced load scenarios. These simulated conditions impart pragmatic insights, revealing potential performance limitations prior to product launch. NeoLoad authenticates simulations across abundant user profiles and traffic patterns, validating capabilities under realistic operational stresses. Comprehensive metrics capture system reactions to the constructed demands. Concurrently, intuitive reporting transforms metric data into actionable optimization guidance. By eliminating barriers between discrete testing tasks, NeoLoad promotes efficient assurance that product functionality can dependably withstand diverse end-user behaviors.
• JMeter is an open-source load testing tool that provides powerful capabilities to simulate high user loads and analyze system performance under changing traffic conditions. It enables users to model numerous concurrent virtual users to impose realistic loads on the application or system under test
• LoadView is a cloud-based platform that offers features specifically designed for stress testing, allowing you to push systems beyond their normal limits and identify breaking points.

Types of reliability testing with examples

Load testing

Load testing gives organizations confidence that their systems can perform resiliently even when receiving large volumes of simultaneous requests. By utilizing load and performance testing tools to methodically stress infrastructure and app functionality to imitate maximum projected workloads, it identifies any limits or weaknesses before they impact real users.

Load testing aims to evaluate how a system performs under regular operating conditions by replicating expected usage at typical levels, such as during peak hours or with projected user growth. Discovering any bottlenecks or lagging during load testing helps ensure the software satisfies the expected performance benchmarks for responsiveness as defined in service level contracts. Ideally, the test environment remains operational during load testing as it replicates normal user conditions rather than pushing a system to failure.

A movie streaming service might perform load testing by simulating millions of users logging in and streaming videos simultaneously. This helps guarantee that the platform can handle high demand during a new season premiere or a major sporting event without buffering or crashing.

Load testing gives organization confidence that their systems can perform resiliently even when receiving large volume of simultaneous requests.

Stress testing

Stress testing simulates overwhelming user loads that exceed a system’s normal capacity to determine its limits under extreme pressure. The aim is to find weaknesses that could occur during events like cyberattacks and ensure issues are addressed to avoid real outages.

An e-commerce site may perform stress testing by modeling millions of parallel login attempts and product page views. This can help determine if the website can tolerate immense traffic loads during a sales promotion or if it’s vulnerable to a DDoS attack targeting the login process.

By modeling a DDoS attack, you can recognize weaknesses in advance of a real assault. If necessary, you can scale the infrastructure or deploy DDoS safeguard solutions to guarantee continuing accessibility even when confronted with heavy traffic loads.

Recovery testing

Recovery testing focuses on how well the system bounces back from a successful cyberattack. It tests the system’s ability to restore lost data, identify and contain the breach, and resume operations efficiently.

Recovery testing centers around how well the system can recover from being successfully targeted. It assesses the ability to regain lost data, identify and isolate the security gap, and efficiently restart standard functions. Subjecting a system to simulated attacks and evaluating the restoration process can help ensure smooth continuity of services even after facing real security threats.

Stability testing

Stability testing involves continuously running the system for extended, long-haul durations, mimicking genuine real-world usage trends over weeks or months. It can help you recognize gradual performance deterioration or memory leaks that may not be immediately visible during short-term testing. The aim is to confirm that the software preserves its dependability and responsiveness even when subjected to prolonged utilization.

Video editing software might undergo stability testing by simulating editing sessions over a long period. This can help identify memory leaks or gradual performance issues that could arise during long projects, ensuring the software remains stable and responsive for users who rely on it for extended periods.

The stages of reliability testing are: Pre-test preparation, conducting, and post-test analysis

What are the stages of reliability testing?

The following are the stages of reliability testing:

1. Pre-test preparation:

This initial stage focuses on laying the foundation for a strategic testing approach. Key activities typically involve the following:

• Careful consideration to defining the objectives, scope, and intended coverage of the assessment
• Establishing performance criteria that provide guidelines around acceptable response times and resource utilization levels
• Creating relevant data sets and realistic usage scenarios that simulate genuine operational conditions
• Using appropriate tools to carry out the testing processes

2. Conducting the test:

With plans in place, this executional phase sees testing come to life. Some common components involve the following:

• Executing predefined scenarios to methodically probe functionality and apply simulated load
• Maintaining close watch over metrics like speeds, utilization, and errors throughout testing runs
• Capturing and documenting all pertinent results data, logs, and observations

3. Post-test analysis:

This evaluation stage aims to gain actionable insights. Typical activities might include the following:

• Analyzing collected data to pinpoint any bottlenecks, issues, or shortcomings uncovered
• Forming sound conclusions about whether standards were fulfilled
• Developing reports encapsulating processes, discoveries, and optimization guidance
• Addressing weaknesses through remedies like fixes, tweaks, and added protection measures
• Best practices for reliability testing

Some best practices for reliability testing include the following:

• Craft test plans: Use performance testing tools to design simulations mirroring actual usage under realistic strain and high-demand conditions.
• Employ authentic data: Use data that closely resembles that which you would use in production environments to uncover potential glitches.
• Automate testing: Rely on automated tests to consistently monitor performance integrating within development pipelines.
• Track testing: Implement thorough incident recording and live analytics to oversee resilience under duress.
• Initiate faults: Intentionally insert malfunctions to ensure graceful endurance and recuperation when issues arise.
• Prolonged testing: Endure grueling marathons to identify long-term difficulties like data leakage.
• Build backups: Put fail-safes in place and automatic substitutes to manage breakdowns.
• Inspect and modernize: Regularly audit and update evaluation techniques to reflect evolving necessities.

This post was written by Chinyere Ordor. Chinyere is a versatile writer and developer with expertise in RPA, backend development, and various other cutting-edge technology fields. With a passion for technology and innovation, Chinyere has written various articles on Node.js, test automation, etc. showcasing her deep understanding of the subject. When she’s not working, she’s found catching up with friends.