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Mean Time Between Failures (MTBF) Explained

Last updated on
October 4, 2023



Mean Time Between Failures (MTBF) Explained


Measuring the Mean Time Between Failures (MTBF) metric is a crucial aspect of maintaining and improving software delivery performance. Let’s explore the benefits of measuring MTBF, as well as its limitations, how to measure it, and alternatives to consider. Check the examples and practical advice on how to improve your MTBF score.

What is Mean Time Between Failures?

Mean Time Between Failures (MTBF) represents the average amount of time that passes between software failures or crashes. It is calculated by taking the total uptime of a system or component and dividing it by the total number of failures.

MTBF is often used to measure the reliability of a system or component and is an important metric for determining how often maintenance or updates should be performed. In general, a higher MTBF is desirable, as it means the system or component is more reliable and requires less frequent maintenance.

However, it's important to note that MTBF doesn’t take into account the severity of failures or the impact they may have on users or business operations. Therefore, it should be used in conjunction with other metrics, such as Mean Time To Repair (MTTR) and Mean Time To Detect (MTTD), to provide a more complete picture of system reliability.

What are the benefits of measuring MTBF?

Measuring Mean Time Between Failures (MTBF) provides valuable insights into the reliability of a system or product:

  1. You can identify the weak points in your system and take corrective action to improve its overall reliability. This helps to prevent downtime, reduce repair and maintenance costs, and improve customer satisfaction. 
  2. Improving MTBF leads to having a more reliable system with fewer failures.
  3. It also leads to improved customer satisfaction, higher productivity, and increased revenue due to fewer disruptions in service. 
  4. Additionally, improving MTBF can help identify and address underlying issues that may be causing frequent failures, leading to overall improvement in the quality of the product or system.

What are the limitations of MTBF?

MTBF has several limitations that can affect its accuracy and usefulness as a metric. Here are some of them:

It assumes constant failure rates

MTBF assumes that failures occur at a constant rate over time, which may not always be the case in real-world systems. Some components may be more likely to fail at certain times or under certain conditions, leading to variations in failure rates that can affect the accuracy of the metric.

It doesn’t consider repair times

MTBF only measures the time between failures, but it does not take into account the time it takes to repair or replace the failed component. This means that a system with a high MTBF may still experience significant downtime if the repair process is slow or inefficient.

It may not be relevant for all types of systems

MTBF is most commonly used for hardware systems, where component failures are a primary concern. However, it may not be as useful for software systems, where failures may be more related to bugs or other issues that are not directly tied to specific components.

It may be affected by maintenance practices

MTBF can be affected by the maintenance practices used for a system. For example, if a system is regularly maintained and components are replaced before they fail, the MTBF may be artificially high.

How to measure MTBF?

MTBF can be calculated by dividing the total operating time by the number of failures over a specific period. For example, if a system has been running for 1,000 hours and experienced 5 failures during that time, the MTBF would be calculated as 1,000 / 5 = 200 hours.

Another approach is to use the following formula:

<span class="colorbox1" fs-test-element="box1"><p>MTBF = (Total operating time – Total downtime) / Number of failures</p></span>

For example, if a system has been running for 1,000 hours, experienced 10 hours of downtime due to maintenance, and had 2 failures during that time, the MTBF would be calculated as (1,000 – 10) / 2 = 495 hours.

MTBF alternatives

One alternative to MTBF is MTTR (Mean Time To Repair), which measures the average time it takes to repair a failure once it occurs. Another alternative is Availability, which measures the percentage of time that a system is available for use. These metrics can be useful in conjunction with MTBF to provide a more complete picture of system reliability and availability.

In software development, MTBF can be useful for identifying patterns of failures and potential areas for improvement in the system. However, it may not always be the best choice for measuring the performance of software systems, as software failures can be more complex and harder to define than physical failures.

Alternative metrics, such as those focused on user experience or business outcomes, may be more appropriate for measuring the effectiveness of software systems in certain contexts. For example, if the goal is to improve customer satisfaction with a web application, metrics such as time to complete tasks, number of errors encountered, and overall satisfaction ratings may be more relevant than MTBF.


MTBF can help you identify the reliability of your product, plan maintenance schedules, and improve the overall quality of software. However, MTBF has limitations, such as not accounting for the severity of failures, and may not be appropriate for all types of systems. Therefore, it’s important to consider other metrics in conjunction with MTBF to get a complete picture of software reliability. 

You should carefully choose the metrics that best fit your team’s needs and goals, and regularly review and refine your measurement approach to ensure you are getting the most value out of your metrics program. The first step is to get a detailed understanding of software delivery metrics. Check other articles in this handbook to do so.

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Olga Gierszal
IT Outsourcing Market Analyst & Software Engineering Editor

Software development enthusiast with 6 years of professional experience in the tech industry. Experienced in outsourcing and nearshoring market analysis. Our expert in presenting tech, business, and digital topics in an accessible way.

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