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Engineering metric of the week: Ticket Complexity

Plandek, June 18, 2021

As the name suggests, ticket complexity allows you to understand the relative complexity of your tickets using 3 different possible Units of Measurement, all based on the commits linked to your tickets.

Unit of Measurement

Definition

Changed Lines

Total inserted and deleted lines made in commits related to each ticket

Developers

Number of unique individuals active on the commits and pull requests related to each ticket

Repositories

Number of unique repositories affected by commits related to each ticket

This metric includes all completed tickets within the selected time range that are linked to any commits.

Ticket Complexity – as shown in Plandek Delivery Efficiency dashboard

Ticket Complexity Metric Chart

Ticket Complexity – examples

Ticket Complexity by Story Points

Below we look at the complexity of tickets based on the number of developers involved. When breaking down this complexity score (average developers) by Story Points we see a strong correlation between the story points value and complexity.

This, in turn, highlights very effective estimation by this team, where their estimates are able to correctly predict the actual complexity of the work.

Ticket Complexity by Story Points – example Plandek metric visualisation

Ticket Complexity Metric Chart

Ticket Complexity – Changed Lines for different Issue Types

Using the Changed Lines unit of measurement and then breaking down the metric by Issue Type we can start to understand the relative complexity of different types of change.

Below, when looking over time, we can see a fairly consistent complexity of the Feature issue type, compared to the more sporadic complexity of Technical Improvements.

Ticket Complexity – Changed Lines by Issue Type – example Plandek metric drill-down chart

Ticket Complexity Metric

Related metrics

Ticket Complexity is an important delivery efficiency metric. Other delivery efficiency metrics that are relevant to be viewed in tandem include:

  • Work in Progress (WIP) – which reflects the number of work items started but not finished (according to the Scrum Team’s definition of “Workflow”)
  • Code Cycle Time (also known as Lead Time for Changes) looks at all completed Pull Requests (e.g. closed, merged, declined etc) within the specified time range and shows the average hours to complete, from when the PR was opened. Not only that but it provides full insight into the different stages that a PR goes through.
  • Flow Efficiency – a broader delivery efficiency metric which looks at the proportion of time a ticket remains in an ‘active’ versus ‘inactive’ state across a Cycle Time or Lead Time.

About Plandek

Plandek works by mining data from toolsets used by delivery teams (such as Jira, Git, CI/CD tools and Slack), to provide end-to-end delivery metrics/analytics to optimise software delivery predictability, risk management and process improvement.

Plandek’s metrics, data visualisations and insights are not available from plug-ins (e.g. Jira plug-ins) as they require collation of data from multiple data sources across toolsets.

Plandek is a global leader in this fast-growing field, recognised by Gartner as a top nine global vendor in their DevOps Value Stream Management Market Guide (published in Sept 2020).

Plandek is based in London and works with clients globally to apply predictive data analytics and machine learning to deliver software more effectively.

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