21 Software Development Metrics Leaders Should Track in 2025

If you’re not tracking the right metrics, your development team is flying blind. You think your team is delivering value and crushing it on speed and quality, but without the data to back it up, you’re just guessing. And in this game, guessing doesn’t cut it.

Metrics are your secret weapon. They give you the power to ship faster, improve code quality, and avoid late-night firefighting caused by technical debt. With the right metrics, you can spot bottlenecks, optimize workflows, and keep your team aligned with real business goals.

Many teams focus on vanity metrics that look good on dashboards but offer little insight into performance or impact. This leads to misaligned priorities, inefficiencies, and cycles of slow releases and escalating tech debt.

That’s where this blog comes in. We’ve got 20 must-track metrics that will change how you run your team—metrics that cover everything from code quality to engineering velocity, productivity, and technical debt management.

This isn’t theory. It’s actionable. By the time you finish reading, you’ll know exactly what to track, why it matters, and how to use these insights to turn your team into an unstoppable delivery machine.

Hot tip: Make sure you use a software engineering dashboard that tracks these metrics for you.

Code quality metrics

When it comes to building solid software, code quality is king. Poor code leads to fragile systems, endless firefighting, and long nights fixing avoidable bugs. These metrics give you a direct line of sight into your code’s health and help you catch problems before they explode.

1. Change Failure Rate

Why it Matters: High change failure rates are a red flag for unstable deployments, poor testing, and rushed merges. Every failed deployment means downtime, firefighting, and frustrated users. This metric is critical for understanding how reliable your release process really is.

How to Use It: Track change failure trends over time. If failures rise, it’s time to level up your testing, enforce stricter code reviews, and refine your CI/CD pipeline to catch issues earlier.

Calculation:

Change Failure Rate= (Failed Deployments / Total Deployments) ×100%

2. PRs Merged without Review

Merging pull requests without review is a fast track to disaster. Bugs slip through, tech debt piles up, and code quality nosedives. Skipping reviews may seem efficient, but it’ll cost you in the long run with fragile systems and costly rework.

How to Use It: Watch this metric like a hawk. Enforce a strict “no unreviewed PRs” policy and set automated checks to catch violations. Build a culture where every PR gets the attention it deserves.

Calculation:

Unreviewed PRs Percentage= (Unreviewed PRs / Total PRs) ×100%

3. Issues Caught in Reviews

The review process is your first line of defense against sloppy code. Catching issues early saves time, reduces bugs in production, and keeps your team from firefighting later. More issues caught in reviews mean fewer headaches post-deploy.

How to Use It: Track the number of issues flagged during reviews. A consistent increase signals thorough reviews, while a drop could mean reviewers are rushing. Pair this with other metrics like change failure rate for deeper insight.

Calculation:

Issues Caught Per PR = Total Issues Caught / Reviewed PRs

4. Rework and Refactor Trends

Rework and refactoring are part of the game, but too much means you’re fixing more than building. Constant rework drains velocity, eats into sprint capacity, and signals deeper issues like unclear requirements or sloppy code practices.

How to Use It: Balance is key. Track how much time goes into rework versus new feature development. If refactoring spikes, zoom in on the root cause—bad planning? Tech debt? Misaligned priorities? Fix the system, not just the symptoms.

Calculation:

Rework Percentage = (Time on Rework​ / Total Development Time) × 100%

Engineering velocity metrics

Speed matters. If your team isn’t delivering quickly, your competition will. Engineering velocity metrics show how efficiently work moves from idea to production. Track these, and you’ll expose bottlenecks, cut waste, and ship value faster—without sacrificing quality.

5. PR Cycle Time

Long PR cycle times kill momentum and slow delivery. The faster your team moves PRs through coding, review, and deployment, the faster you deliver value.

How to Use It: Break down cycle time into phases: coding, pickup, review, and deployment. Track where delays happen and tackle them head-on. Shorten coding cycles, speed up reviews, and automate deployments to slash cycle time.

Calculation:

PR Cycle Time = Deployment Date − PR Creation Date

6. Deploy Frequency​

The more often you deploy, the faster you get feedback, reduce risks, and deliver value. High deploy frequency keeps your team agile, allowing for quick iteration and faster fixes. If you’re deploying once a month, you’re already losing.

How to Use It: Aim for small, frequent deployments. Track how often code hits production, and focus on improving CI/CD pipelines to speed things up. Lower frequency? Check for bottlenecks in reviews, testing, or deployment processes.

Calculation:

Deploy Frequency = Total Deployments​ / Time Period (Days)

7. Daily WIP (Work in Progress)​

Too many tasks in progress? Your team’s juggling too much, and progress grinds to a halt. High WIP means more context-switching, slower delivery, and unfinished work piling up. Keep it lean and focused to maintain flow and velocity.

How to Use It: Track daily active tasks per squad or player. If WIP is high, limit tasks in progress and prioritize finishing over starting. Use this for daily standups to keep everyone aligned and workloads balanced.

Calculation:

Daily WIP = Total Active Tasks on a Given Day

8. Issue Cycle Time per Task Type​

Issue cycle time shows how fast your team resolves tasks. Long cycle times slow delivery, frustrate users, and hint at deeper workflow problems. Breaking it down by task type—features, bugs, hotfixes—gives you clarity on where things get stuck.

How to Use It: Track cycle time for each task type. Slow bug fixes? You’ve got QA gaps. Features dragging? Check for vague requirements. Use this metric to target and eliminate bottlenecks.

Calculation:

Issue Cycle Time = Date Done−Date In Progress

Technical debt metrics

Technical debt is the silent killer of engineering teams. It slows development, increases failure rates, and turns simple changes into all-night coding marathons. These metrics help you keep debt in check, so you can focus on shipping features, not firefighting broken code.

9. Rework Percentage​

Rework is a productivity killer. High rework means your team spends more time fixing old mistakes than building new features. It’s a clear sign of poor planning or rushed work.

How to Use It: Track rework trends over time. If it spikes, investigate root causes—unclear requirements, weak testing, or skipped reviews—and tighten your processes.

Calculation:

Rework Percentage = (Time on Rework / Total Development Time) ​×100%

10. Refactoring Trends​

Refactoring is essential for keeping your codebase clean and maintainable. But if refactoring dominates your sprint, you’re not moving forward—you’re stuck in code cleanup mode. Balancing refactoring with new features is key to maintaining velocity.

How to Use It: Track how much time is spent on refactoring versus building new features. If refactoring trends spike, prioritize the high-impact areas first. Avoid endless tinkering—refactor with a purpose that aligns with business goals.

Calculation:

Refactoring Percentage = (Time on Refactoring​ / Total Development Time) ×100%

11. Technical Debt Ratio

When more time goes into refactoring than building new features, your team’s buried in debt. This ratio shows how much tech debt is holding you back. A high ratio means you’re cleaning up more than you’re innovating—a clear sign it’s time to fix underlying issues.

How to Use It: Track this ratio regularly. If refactoring starts dominating, dig into high-debt areas and address root causes like poor architecture or weak testing. Balance is everything.

Calculation:

Technical Debt Ratio = Time on Refactoring​ / Time on New Features

Productivity and collaboration metrics

Productivity isn’t about churning out code—it’s about maintaining a steady, sustainable flow of high-quality work. Collaboration is the fuel that keeps that flow moving. These metrics help you spot blockers, streamline reviews, and ensure your team stays productive without burning out.

12. PRs Opened vs. PRs Merged​

A growing gap between PRs opened and merged means bottlenecks. Work piles up, reviews get delayed, and your pipeline grinds to a halt. Balance is the goal.

How to Use It: Monitor this ratio weekly. A widening gap? Check for overloaded reviewers or oversized PRs. Tighten review processes and break large PRs into smaller chunks.

Calculation:

PR Ratio = PRs Opened / PRs Merged

13. Comments per Review​

Comments are where the real collaboration happens. A high number of comments means engaged reviewers, deeper code discussions, and fewer bugs slipping through. When comments drop, it’s a red flag for rushed or superficial reviews.

How to Use It: Track the average comments per review. If they decline, encourage more thoughtful feedback and set review standards. Promote a culture of constructive criticism to keep quality high. Balance is key—too many comments can slow things down, but too few means trouble.

Calculation:

Comments per Review = Total Comments​ / Total Reviews

14. Days with Commits​

Consistent commits show steady progress and a healthy development rhythm. Gaps in activity could mean blockers, disengagement, or uneven workloads. More commit days = smoother releases and fewer fire drills.

How to Use It: Track commit activity across your team. If commit days drop, check for bottlenecks or overloaded devs. Encourage smaller, daily commits to keep the workflow moving and reduce massive, risky code dumps.

Calculation:

Days with Commits = Unique Days with Commits in a Given Timeframe

15. Unreviewed PRs​

An unreviewed PR is a ticking time bomb. Code merges without review can introduce bugs, increase technical debt, and compromise stability. It might feel faster, but you’ll pay for it later in fixes and downtime.

How to Use It: Monitor unreviewed PRs and set a zero-tolerance policy. Use automation to enforce reviews and flag exceptions. Keep review standards tight and scalable, even during crunch time.

Calculation:

Unreviewed PRs Percentage = (Unreviewed PRs / Total PRs) ​ × 100%

Deployment and reliability metrics

Fast, reliable deployments are the backbone of high-performing teams. Frequent releases mean faster feedback, quicker iterations, and fewer risky, big-bang deployments. These metrics help you monitor release stability, reduce failure rates, and keep your deployment pipeline smooth and predictable.

16. Deployment Frequency

Frequent deployments mean shorter feedback loops and less risk. Teams that deploy regularly can adapt quickly, ship features faster, and stay ahead of the competition.

How to Use It: Track how often code hits production. If frequency drops, look for bottlenecks in testing or review. Improve your CI/CD pipeline to boost deploy frequency.

Calculation:

Deployment Frequency = Total Deployments​ / Time Period (Days)

17. Change Failure Rate (DORA Metric)

This is your go-to metric for stability. A high change failure rate signals weak testing, rushed deployments, or tech debt lurking in the shadows. Every failed deployment means downtime, lost productivity, and unhappy users.

How to Use It: Keep a close eye on failure trends. If failure rates climb, focus on improving test coverage, tightening review processes, and automating deployment checks. Fix the cracks before they become sinkholes.

Calculation:

Change Failure Rate = (Failed Deployments / Total Deployments) ​×100%

18. Mean Time to Recovery (MTTR)

Outages happen, but how fast you recover is what really counts. MTTR measures how quickly your team restores service after a failure. The shorter your MTTR, the less impact on users and the faster you get back to shipping features.

How to Use It: Track MTTR to gauge incident response efficiency. If it’s high, focus on improving your monitoring, alerting, and incident response playbooks. Drill down on patterns to cut response times.

Calculation:

MTTR = Total Downtime (Minutes)​ / Number of Incidents

19. Code Coverage Percentage

Code coverage tells you how much of your code is tested. Low coverage means hidden bugs and higher failure risks. It’s not about hitting 100%—it’s about making sure critical paths are covered to reduce nasty surprises in production.

How to Use It: Focus on covering business-critical functions and high-risk areas. Track trends over time, not just the raw percentage. If coverage dips, prioritize writing tests for recent changes. Balance is key—more tests, but not at the cost of delivery speed.

Calculation:

Code Coverage= (Covered Lines / Total Lines of Code) ​×100%

20. Business Alignment Metrics

If your team spends more time firefighting than building new features, you’ve got a problem. Business alignment metrics track how much work goes toward strategic goals versus unplanned distractions. It’s the key to keeping your team focused on what really matters.

How to Use It: Regularly check how effort is split between roadmap work, planned maintenance, and unplanned tasks. If unplanned work spikes, investigate and recalibrate priorities to stay on track.

Calculation:

Alignment Ratio = (Roadmap Work Hours / Total Work Hours​) ×100%

21. Sprint Predictability

Predictable sprints mean your team delivers what they commit to—on time and without surprises. When predictability drops, deadlines slip, priorities shift, and trust erodes fast.

How to Use It: Track story points or completed tasks versus planned work for each sprint. High predictability? You’ve nailed your planning. Frequent carryovers? Adjust your sprint goals or check for bottlenecks. Improve reliability by breaking work into smaller, manageable chunks.

Calculation:

Sprint Predictability = (Completed Work / Planned Work) ​×100%

Best practices for using these metrics effectively

Follow these guidelines to make the most of your data.

Use an engineering metrics dashboard

Centralize your metrics with a powerful dashboard like DevStats to get real-time insights into performance. A dashboard eliminates manual tracking, reduces errors, and provides clear visualizations that help your team make data-driven decisions fast.

Avoid common pitfalls

• Misinterpreting Metrics: Focus on trends and patterns, not isolated data points.
• Vanity Metrics: Ignore numbers that look impressive but don’t drive value (e.g., total commits without context).
• Micromanagement: Use metrics to empower your team, not to control every move.
  

Align metrics with business goals

Metrics should support your business objectives. Tie code quality, velocity, and collaboration metrics to broader goals like faster feature delivery, improved user experience, or revenue growth. This ensures your team is always working on what matters most.

Follow a continuous improvement cycle

Treat metrics as part of a feedback loop.

• Identify problem areas.
• Plan realistic, data-backed improvements.
• Execute necessary changes.
• Review progress regularly. Iterate and repeat. This keeps your team evolving and always moving toward better performance.

Tracking the right metrics isn’t just about numbers—it’s about building a smarter, faster, and more reliable engineering team that consistently delivers real impact.

Track key metrics daily. Check out DevStats.