Metrics & Methods for Improving Engineering Performance

ionicons-v5-kBy JustinJune 17th, 2022

As an engineering manager, it is crucial to measure your team's performance to identify areas that may need improvement. There are a variety of metrics that can be used to do this, and in this article, we will discuss three of the most important ones.

Lead Time

A few key metrics can help you understand and improve your engineering team's performance. One of the most important is lead time - the time from when a new feature is first described to when it's finally implemented in production.

Many factors can affect lead time, including process bottlenecks, lack of ownership, or unclear descriptions. Another crucial element is the level of automation. The more automated your process, the shorter your lead time will be.

Lead time is a valuable metric for product teams, as it can help plan new features and development timelines. Understanding your team's lead time and taking steps to improve it can significantly impact your engineering team's overall performance.

Methods for Improving Lead Time

There are a few critical methods for reducing lead time and improving engineering performance. One is to focus on automating as much of the process as possible. This can help eliminate bottlenecks and improve efficiency. Another critical method is to make sure that there is clear ownership of each step in the process. This will help ensure that each step is carried out effectively and efficiently. Finally, it's essential to provide precise descriptions of each step in the process. This will help everyone involved understand what needs to be done and how it fits into the overall plan.

Codebase Issues Resolved

As your codebase evolves, it is vital to track the number of codebase issues that are resolved. This metric will show you whether your team is keeping up with the codebase's maintenance and whether they can quickly resolve technical debt and other codebase issues.

Code Quality Metrics

You need to track multiple code quality metrics to get an accurate picture of your codebase quality. Some of the most essential code quality metrics are:

Cyclomatic Complexity

This metric measures the complexity of a piece of code and is a good indicator of its maintainability. A high cyclomatic complexity indicates that the code is difficult to understand and maintain.

Maintainability Index

The maintainability index is a composite metric that considers several code quality metrics, such as cyclomatic complexity and code coverage. A high maintainability index indicates that the code is easy to understand and maintain.

Code Coverage

Code coverage measures how much of the code is covered by tests. A high code coverage indicates that the code is well tested and is less likely to contain bugs.

Number of Bugs

The number of bugs is a good indicator of the quality of the code. A high number of bugs indicates that the code is poorly written and is more likely to cause problems.

Number of pull requests to story points

As a team, we prefer to count the number of story points that developers complete per sprint rather than the raw number of pull requests. This is because it more fairly and accurately represents our team's velocity since it considers both the quantity and difficulty/complexity of each issue.

On top of that, some developers prefer to create smaller PRs, while senior developers will often complete more PRs. Therefore, by using story points as our primary metric, we can more easily compare and track progress week-by-week.

Assigning story points to each issue to start tracking story points, every issue must first be assigned a certain number of points. This can be done using any agreed-upon system, but we find the following to be most effective:

  • 1 point = A task that can be completed in one day or less
  • 2 points = A task that will take two days or more to complete
  • 3 points = A particularly large or complex task that will take a full sprint or more to complete.

Once all issues have been assigned story points, we can begin tracking our team's total velocity by summing up the total number of points completed each sprint.

Using story points to improve engineering performance

An accurate measure of your team's velocity is essential for proper planning of future sprints and projects. If your team is consistently under- or over-performing, you can use story points to help identify areas of improvement.

For example, if you notice that your team regularly completed fewer than the average number of story points per sprint, it could indicate that your issues are too large or complex. In this case, you may consider splitting some of your issues into smaller tasks.

On the other hand, if your team is consistently completing more story points than average, it could indicate that your issues are too small or simple. In this case, you may want to consider combining some of your issues into larger tasks.

Making these adjustments can help ensure that your team is set up for success and can properly plan and execute each sprint.

In Summary

There are a variety of metrics that can be used to measure the performance of your engineering team. This article discussed three of the most important ones: cyclomatic complexity, maintainability index, and code coverage. We also discussed how you can use story points to improve your team's velocity and performance. You can help ensure that your team is continuously operating at its best by tracking these metrics and making adjustments as needed.

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