5 Key Metrics To Fix Your Software Team's Quality
What does "quality" really mean to you as a leader in engineering?
We get stuck on old ways of measuring things too often, like uptime or how many bugs were fixed. But that's a limited view. Let's look at it differently.
Quality is the most important thing that makes your team work better. When you strategically measure and improve quality, you don't just get better code; you also get an engineering organization that works better, is more predictable, and performs better.
Why Quality Is Your Team's Ultimate Advantage
To put it bluntly, quality is the most important thing for your engineering team. One way gives you a high-efficiency advantage, and the other way gives you a low-efficiency disadvantage.
A cycle of good behavior begins with high quality. Teams get more done, meet deadlines more often, and spend less time putting out fires. This makes room for ongoing growth and new ideas, which makes developers happier and more involved and customers happier. It's a loop of success that keeps going.
On the other hand, low quality starts a bad cycle. Teams miss deadlines, get stuck on customer problems, and don't have time to make things better. Developers lose motivation because they feel like they're always running on a treadmill to keep things from falling apart. This cycle stops growth and wears out your best workers.
How to Measure Quality for Real Efficiency Gains
So, how do you get on the right track? It all starts with measuring the right things. There are two kinds of indicators in engineering: lagging and leading. You need both, but focusing on the leading indicators will give you an edge over your competitors.
The Rear-View Mirror: Lagging Quality Indicators
The classic metrics are lagging indicators. They are important for comparing things and keeping track of progress over time, but they only show you what has already happened. It's like looking in the rearview mirror: it helps you understand what's going on, but it won't help you avoid the pothole in front of you.
- Issues Found in Production (Change Failure Rate)
This is a classic. It is often called the Change Failure Rate, and it tells you how many defects or problems get past the checks and end up in production. Your team becomes less efficient the more problems you find in production. Why? Because every bug in production is a "all-hands-on-deck" emergency that stops work on new features, messes up your roadmap, and lowers productivity. - Mean Time to Restore (MTTR)
How long does it take to fix something when it breaks? The time it takes to restore is called Mean Time to Restore (MTTR). From an efficiency point of view, MTTR is the time your developers have to stop what they're doing to put out fires. A high MTTR makes it harder to finish projects on time and hurts customer trust.
Leading Indicators: Your Proactive Toolkit
This is where things start to change in the game. Leading indicators show you a little bit about the future. They help you find problems before they affect production, so your team can be proactive instead of reactive.
- Work Breakdown (Especially Rework)
- This is one of the most useful metrics that people don't pay enough attention to. Work Breakdown looks at your codebase to see how much of it is new work, refactored code (which is code that has been improved), and most importantly, reworked code (which is code that has been changed after it was written).
- If you see a lot of rework (more than 20% is a red flag), it's a big sign that the quality is bad. It means that developers are working on the same piece of code over and over again. This could be because the requirements are not clear, the technical design is bad, or the tech debt is building up. If you can find rework, you can fix the problem before it slows down the whole team.
- Branch Risk Detection:
- What if you could guess which branches would cause the most problems before the code was even written? That's what branch risk detection is for. You can find high-risk work before it's even merged by looking at things like how much code is being changed and how much rework is going on in a branch.
- This is where insights powered by AI become very useful. An engineering intelligence platform can automatically find these risky branches, which lets managers step in in a smart way.
Inspect them. Is the large amount of change and rework justified?
- Pull Request Health (The Three D's)
The pull request (PR) process is full of great ways to measure quality. Three things are important to me:
- How many Long-Living PRs (open for more than a few days) do you have? These are often signs of blockers, changes that are too complicated, or a review process that has stopped.
- How deep is your PR review? This shows how many comments there are on average for each PR. If there aren't enough comments, it could mean that the quality problems are being missed.
Danger:How many "Lightning PRs" are there? These are PRs that are merged in minutes with little to no review. These are dangerous and can often lead to problems with production in the future.
- Identify long-living PRs and find out what's blocking them.
- If your review depth is low, work with your team to establish better review standards.
- Look at lightning PRs—are reviewers being thorough, or just clicking "approve"?
Conclusion: Shift from Reactive to Proactive
To build an elite engineering organization, you have to look at quality through the lens of efficiency. While lagging indicators like MTTR and Change Failure Rate are important for benchmarking, the real magic happens when you focus on leading indicators.
By proactively managing Rework, Branch Risk, and Pull Request Health, you can stop fighting fires and start building a well-oiled machine that delivers more value, keeps customers happy, and empowers your developers to do their best work.
Tired of flying blind?
EvolveDev.io provides the visibility and AI-powered insights to track these metrics automatically, helping you build a culture of engineering excellence. See how it works today.
