Delivery Performance
9 minutes reading time
A few years ago, growth was about hiring more people. Today, growth is about efficiency—doing more with less. As an Engineering Leader, you must align your teams to achieve business goals while improving efficiency. Not every organization has the luxury of hiring more people, so what do we do? We capture each source of inefficiency and thrive on removing them.
After working with several organizations over the years, we have noticed common pitfalls that slow teams down. More importantly, we prepared a list of recommendations to avoid them.
Pull Requests Are Stuck or Taking Too Long to Review
Many engineers think they’re productive when busy. Instead of waiting, they tend to send their pull requests to a colleague for review and start a new work item. The colleague reviews the pull request once they finish their task, so the pull request is idle, causing delays in the value of delivery. The results? Pull requests are accumulating, review time is increasing, and value takes longer to capture.
How to Spot The Trend?
You can measure the lifecycle of how long it takes to go from a commit to its deployment. The metric is called the Lead Time for Changes and is one of the four key DORA metrics in DevOps. You can split the lifecycle into stages and spot trends. For example, here is an image where you can see the coding time (from a commit to its PR opening), the pickup time (from the opening of a PR to its first interaction), the review time (from the first interaction until it’s merged) and finally the deployment time (from the time it’s merged until it’s deployed in production).
Splitting the lifecycle into phases makes it easier to find the bottleneck. A typical behaviour we observe is when review time increases. It’s a sign that engineers open more pull requests without prioritizing them or that the PRs are too big, so engineers avoid reviewing them.
Solution
In this situation, there are some options to consider:
- Make reviewing pull requests a priority. After a daily, everyone should first merge pull requests before tackling new work.
- Reduce the work in progress (WIP) in the team. Teams experience delays because they’re waiting for or depending on someone. You can’t wait for somebody if you work together. Reducing WIP tends to improve cycle time.
- Encourage smaller pull requests. When a pull request is too big, engineers need more time to review it and tend to avoid it. Instead, favour one change per pull request.
How to Get Started
The first step is to start measuring yourself. The first set of metrics I suggest gathering is the DORA metrics. This will give you a first picture of the team’s delivery performance and let you know which teams need more attention from the pack.
Setting up Your DORA Metrics Dashboard
Tools such as Axify integrate seamlessly with your tech stack to collect accurate data at all phases of development. Our DORA metrics dashboard tracks Deployment Frequency, Lead Time for Changes, Change Failure Rate and Failed Deployment Recovery Time. It allows teams to compare their performance with industry benchmarks, past performance, and other teams in the same organization to identify areas for improvement and celebrate successes.
Comparing Your Teams’ Delivery Performance
Our teams’ insights allow you to visualize DORA metrics for each team. They offer the advantage of comparing apples to apples on two important engineering efficiency factors: speed and stability. You can quickly see which team could benefit from more attention and which could share their best practices for better performance.
Working Toward Continuous Improvement
Transform how your team sets and achieves goals with our objective and key results tracking tool. See immediately the evolution of your performance indicators and implement initiatives that support the continuous improvement of your development team.
Contact us for more information on how Axify helps development teams measure DORA KPIs and improve engineering efficiency, or request a demo.
More Pitfalls to Avoid for Maximum Software Engineering Efficiency
- Working items are too big
- Quality control is long due to a lack of automation
- Bad allocation of time investment
- Working on too many items at once
Or download our complete guide 👇