Dashboard for Mean Time to Detect
Mean time to detect (MTTD) tells you how much time it takes a team to discover and start to remedy a bug. Try not to confuse this with things like mean time to recovery, as MTTD does not include the time it takes to fix a bug or to get a program fully functional again. This just tells you how quickly DevOps teams work to discover bugs and how well your monitoring systems are detecting issues. This metric can be used by DevOps teams and management alike, creating valuable insight into how quickly bugs are being addressed. If there’s a pattern of bugs taking a long time to be addressed, this might highlight an opportunity to implement new detection techniques, either on the dev side of things or the monitoring systems in place.
How to Measure Mean Time to Detect
Learn about Mean Time to Detect, including how to measure it, and leverage it in dashboards and visualizations with Metabase.
What is Mean Time to Detect?
Mean time to detect (MTTD) tells you how much time it takes a team to discover and start to remedy a bug. Try not to confuse this with things like mean time to recovery, as MTTD does not include the time it takes to fix a bug or to get a program fully functional again. This just tells you how quickly DevOps teams work to discover bugs and how well your monitoring systems are detecting issues. This metric can be used by DevOps teams and management alike, creating valuable insight into how quickly bugs are being addressed. If there’s a pattern of bugs taking a long time to be addressed, this might highlight an opportunity to implement new detection techniques, either on the dev side of things or the monitoring systems in place.
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How to calculate Mean Time to Detect
Mean time to detect can be calculated by taking the time spent searching for bugs and divide it by the number of bugs detected. For example, let’s say 24 hours is spent searching for bugs. At the end of the search, 8 bugs are detected. That means the mean time to detect is 3 hours.
Other KPIs to measure related to Mean Time to Detect
- Response Times
- Online Application Availability
- Lead Time For Changes
- Uptime
- Error Rate
- Cost of New Development
- Mean Time Between Failure
- Online Application Performance
- Time to Value
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