Top 10 Metrics You Need For Measuring Productivity
Considering a large number of metrics available, this article summarizes the top ten metrics to measure from the delivery team's productivity perspective.
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Measuring and tracking the progress of any program is key to success, as quoted in the famous saying:
If you can’t measure it, you can’t improve it.
Considering a large number of metrics available, this article summarizes the top ten metrics to measure from the delivery team's productivity perspective:
|Feature Cycle Time||To measure the time-to-market of a feature to generate business value and denotes the velocity of the team.
It also helps to find out inefficiencies within the team or technology, which can provide visibility to take strategic measures.
|Cycle time from Definition of Doneness to Go Live. It is usually measured in days/weeks.
Feature Cycle Time = Implementation End Date – Implementation Start Date
|Number of Releases||To measure team’s ability to support time-to-market business needs||Number of releases in a month, week, or a day.|
|Sprint Predictability||To measure predicted vs. actual delivery commitment. Program management can use this metric to improve future Sprint’s projection and improve upon providing more realistic timelines to senior stakeholders.||Number of committed stories delivered / Total number of committed stories.|
|Functional Test Execution Time||To measure the time taken to run the progression testing suite as well as regression testing suite. The shift-left strategy applied to ensure functional testing is fast to detect issues early.||Time taken to execute the testing suite (progression and regression).|
|Defect Removal Efficiency (DRE)||To measure the productivity of the development team to remove defects prior to release.||Number of defects resolved by the development team / total number of defects at the moment of measurement.
DRE= (Number of defects found internally/ Number of defects found internally + Number of defects found externally) × 100
|Code Build Time||To measure the DevOps maturity level and ensure that team’s efficiency is not impacted by it.||Time required to build the deployable packet, passing all gates of Continuous Integration cycle. (incl. code quality, security, and performance testing, sanity testing, etc.)|
|Release and Deploy Time||To measure the DevOps maturity from a continuous deployment perspective.||Time required to release the packet on a production-like cluster and bringing the services back for use.|
|Time to create a new environment||To measure the DevOps maturity to build a new environment to enable the development team to focus on issue resolution. The process should be as close to possible as used for a production environment.||Time it takes to create a new environment for the development team. It is measured in minutes or seconds.|
|Time to Fix a Broken Build (Mean time to repair – MTTR)||It reflects how quickly an organization or team can respond to system issues or defects and fix them. It also denotes the architecture and design maturity to enable the team to resolve issues quickly.||Time lag between broken (including compilation or quality gate failure) build to the fixed build.
MTTR = Total time to repair / Total number of repairs
|MTBF (mean time between failures)||To measure the reliability of the system and architecture and design maturity of the system under consideration.
It measures how effectively the team is using SRE practices.
|The 'up-time' between two failure states of a repairable system during operation. It is typically represented in hours. The higher the MTBF number is, the higher the reliability of the system.
MTBF = Total uptime / # of breakdowns
You can also measure the maturity level of your practices for the above metrics. Maturity model definition can vary as per your organization and here is a sample maturity model definition for reference:
To conclude, ensuring the right set of metrics are being tracked is the first step towards improving productivity. Additionally, having a clearly defined maturity model helps to track the progress iteratively.
Published at DZone with permission of Ankur Kumar, DZone MVB. See the original article here.
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