If you lead an engineering team, you already know: picking the right metrics can be the difference between well-informed decisions and shots in the dark. In this article, we go straight to the point: which software metrics actually help leaders guide their teams with clarity and focus. No magic formulas.
Not every software metrics deserves your attention
Just because you can measure it, doesn’t mean it’s worth measuring.
Poorly chosen — or poorly interpreted — metrics create the wrong incentives and get in the way more than they help.
Focusing on “lines of code written,” for example, is an open invitation to inflate PRs with unnecessary solutions. Measuring only “delivery speed” can push the team into rushed decisions that hurt product stability.
Another common mistake is getting stuck on numbers that don’t align with the company’s strategic goals. Metrics like “number of releases per month” might look good on paper but ignore the real impact on user experience or business results.
The key is context: it’s not just about what to measure, but why you’re measuring it, and what you’ll do with that information.
Which Software Metrics Should You Track?
Here’s a curated list of what really matters to track team performance, spot bottlenecks, and keep delivery quality high.
Efficiency Metrics
Cycle Time
It’s the time between the first commit related to a delivery and the moment that delivery hits production.
This metric focuses on the “executable” part of the flow, from writing code to deploy. It includes everything in between: development, PR opening, review, approval, merge, and pipeline.
If it’s high, the problem could be in any of those steps. Stalled PRs, slow reviews, broken builds, or slow deploys. It’s a helpful metric to see where the flow is getting stuck once the work has already started.
Lead Time
Unlike Cycle Time, Lead Time starts before the code exists. It measures the total time from when a task enters the backlog (or gets prioritized) until it’s live in production.
In other words, it includes prioritization queue, refinement, development, and deploy. It’s a more comprehensive metric, great for evaluating how quickly engineering responds to a business need.
Throughput
It measures how many deliveries the team completes in a given time frame — usually per sprint or week. It can be counted in number of tasks, user stories, or whatever unit of work your team uses.
It’s a volume metric, not effort. Two delivered tasks can vary a lot in size, so it’s best to look at the team’s historical patterns over time. A drop in throughput doesn’t necessarily mean lower productivity, but it can be a sign something needs looking into.
Task Completion Rate
Shows the ratio of tasks completed vs. started in a period.
If the team starts 10 tasks in a sprint and finishes only 5, the rate is 50%. This helps you see if the team’s starting more than it can finish — which can lead to too much work in progress, rework, and context loss.
Velocity
Measures how many points (or other effort units you use) the team delivers per iteration. It’s usually used in agile contexts, especially Scrum.
It’s a relative metric, calibrated internally. It’s not meant to compare teams, but to predict delivery capacity and track progress sprint by sprint. Sudden velocity shifts often point to scope, estimation, or focus issues.
Pickup Time
It’s the time between when a PR is opened and when someone starts reviewing it.
This metric shows how long the code sits waiting for attention. PRs that sit too long break the dev’s rhythm, cause context switching, and delay merging. High pickup time may point to a reviewer bottleneck or lack of visibility in the flow.
Review Time
Tracks how long a PR takes from the start of review to merge (or rejection).
It helps you see if the team is reviewing efficiently. If it’s taking too long, maybe the code’s too complex, the PR is too big, or there’s low review availability. It also helps show if the team’s actually engaging in the process or just “approving for the sake of it.”
Deployment Time
Measures the time between PR merge and the code actually going live in production.
It’s directly affected by how efficient your CI/CD pipeline is. If the time’s high, there might be unnecessary manual steps, slow builds, or QA processes that aren’t well aligned with the team. This metric shows how automated (or stuck) your deploy is.
Deployment Frequency
How often the team deploys in a given timeframe — per day, week, or sprint.
The more frequent the deploys, the more granular the feedback. Teams that deploy less tend to pile up big changes, making rollbacks harder, increasing risk, and complicating troubleshooting. There’s no ideal number, but frequency says a lot about how mature your delivery flow is.
Quality Metrics
Code Churn
This metric tracks how much of the newly delivered code was changed or deleted within a short time frame (e.g. 1 or 2 weeks).
Some churn is totally normal — it’s part of tweaking, refactoring, or adjusting the solution. But if churn is consistently high, it might point to rushed decisions, unclear requirements, or low confidence in the first implementation.
The number usually comes as a percentage. For example: if 500 lines were added in a week and 200 of those got changed or removed the next, churn is 40%.
Refactoring Ratio
Tracks how much of the delivered code is tied to changes in existing code — instead of new features.
It helps you understand how much the team is investing in keeping the codebase healthy over time. A healthy amount of refactoring means the team is clearing the path before moving forward. Zero refactoring might mean tech debt is piling up. Too much refactoring might be a sign of rework or hidden churn.
This ratio can be measured by number of commits, lines, or PRs marked as refactor.
Change Failure Rate
Shows the percentage of deploys that result in failure — whether that’s an incident, rollback, or hotfix.
It’s a straightforward metric: how often something pushed to production needed fixing after. A deploy counts as a failure if it caused an error, a bug in the real environment, or needed post-release intervention.
It’s one of the DORA metrics (DevOps Research and Assessment) and usually goes hand in hand with lead time and deployment frequency. The more frequent and reliable the deploy, the better.
Test Coverage
The percentage of code that’s exercised by automated tests.
It’s measured by tools like jest --coverage, SimpleCov, Jacoco, etc. Usually shows what % of lines, statements, branches, or functions are covered.
But coverage is just the start. Having 90% coverage with tests that don’t actually validate real behavior doesn’t mean much. The number helps track if the team is skipping entire areas without tests — but it says nothing about the quality of those tests.
Bug Rate
Number of bugs reported in production per time period, per sprint, or per delivery volume.
This metric usually comes from issue tracking tools (like Jira or Linear) or monitoring platforms (like Sentry or Bugsnag). It shows how often problems slip through the delivery pipeline and reach the end user.
You can break it down by system area, type of feature, or type of error (regression, critical error, etc).
Merged PRs Without Review
Counts how many pull requests got merged without any review interaction. No comments, no approvals.
Basically, this shows when the team is skipping steps. It could be due to rushing, not enough reviewers, or a “full trust” culture (which isn’t always a great idea). This metric helps you see whether the review process is working — or if it’s just there for show.
Mean Time to Repair (MTTR)
Measures how long, on average, the team takes to fix a bug after it’s detected.
The clock starts when the issue is reported (or caught by monitoring) and stops when the fix hits production. It’s not about spotting fast or fixing fast — it’s about the full cycle: understanding, prioritizing, fixing, and shipping.
A high MTTR can point to troubleshooting issues, poor test coverage, slow deploys, or just a badly prioritized backlog.
Review Depth
Measures the average number of comments per PR during the code review process.
The more comments, the more likely it is that technical discussions, solution improvements, or problem spotting are happening. That doesn’t mean more is always better — but a really low average might mean PRs are just flying through with no real review.
This metric also helps you see whether the team’s truly engaging in the process or just hitting “Approve.”
Metric Related to Team Focus
WIP (Work in Progress)
Tracks how many tasks are in progress at the same time — per person or per team.
The goal here is to spot if the team’s taking on more work than it can actually finish. High WIP usually means too much open context, constant focus switching, and not much real delivery.
It’s a simple but powerful metric: it shows if the workflow is healthy or if things are getting stuck before they’re done. You can measure it based on tasks marked as “in progress” on the board (e.g. Kanban, Scrum, etc.).
Interruption Rate
Tracks how many times per week (or per sprint) the team gets interrupted by stuff that wasn’t in the plan.
This can include urgent hotfixes, ad-hoc requests, unscheduled meetings, or anything else that pulls the team away from prioritized work. This metric helps you see how much outside chaos is messing with the team’s focus.
It doesn’t have to be 100% precise. Just track whenever something breaks the team’s rhythm — and how often that happens. Too many interruptions are usually a sign of poorly defined priorities, weak discovery, or no “shield” between the tech team and outside demands.
Team Satisfaction Score
This is a qualitative metric, gathered through quick surveys, regular check-ins, or pulse tools.
It can measure anything from overall job satisfaction to sense of autonomy, alignment with leadership, clarity of goals, and workload. The goal here is to understand how the team’s feeling — because that directly affects delivery.
No need for a 30-question survey. A simple 1-to-5 scale on a few relevant topics already says a lot. The most important part: track it often and cross it with your quantitative metrics. A drop in satisfaction almost always comes before a drop in delivery.
A good software metrics is one that helps you make better decisions
There’s no such thing as a perfect metric — only useful ones.
The real value is in using the data to spot patterns, adjust direction, and keep improving the process. Measuring just to measure only clutters your dashboard. Measuring with purpose improves product, team, and business.
Read the numbers in context. Combine quantitative metrics with qualitative feedback. And never use a single metric in isolation to justify performance.
