Tracking the right software development KPIs has always been tricky. For years, you had leading indicators, lagging indicators, input-based metrics, and output-based metrics. Sorting through all of this was complicated, but it gave engineering leaders at least a vague idea of how things were going. But with the rise of AI-assisted coding and the widespread adoption of platform engineering, the old ways of measuring "productivity" suddenly feel insufficient.
Speed is obviously important, but it isn't the whole story. Engineering leaders also have to consider the health of the system, the happiness of the developers, and the actual impact of the code their teams ship.
As 2025 comes to a close and we look ahead to 2026, let's take a look at the 15 software development KPIs that matter most today, including how AI is changing the baseline for engineering excellence.
Balancing speed and satisfaction with DORA, SPACE, and DevEx
Before we look at specific metrics, we need to talk about the frameworks that house them. You typically can't go wrong with DORA, but modern engineering leaders are increasingly pairing it with SPACE and the DevEx Framework to get a complete picture.
DORA (DevOps Research and Assessment) covers the tension between speed and stability. It measures how frequently you ship and how often those changes cause incidents.
SPACE (Satisfaction, Performance, Activity, Communication, and Efficiency) covers the human factors that drive that work. It measures whether your team has the focus time and psychological safety needed to deliver results.
DX Core 4 is a unified productivity framework that encapsulates DORA, SPACE, and DevEx. It balances Speed, Effectiveness (DevEx), Quality, and Business Impact to ensure you aren't optimizing for one at the expense of the others.
15 KPIs to improve software development
Different organizations prioritize different things, but these 15 KPIs provide a comprehensive view of engineering health in the current landscape.
1. Velocity (with an AI caveat)
Velocity measures a team’s rate of completing user story points during sprints. It helps teams predict how much work they can handle.
This metric requires nuance as Copilot and Cursor become essential tools for most developers. While AI might cause your velocity to spike, remember that higher velocity doesn't always mean more value. If you're shipping more features but they're buggy or the wrong features, AI has just helped you build the wrong thing faster. Use velocity to measure capacity, not productivity.
2. Lead time for changes
Lead time represents the total duration from the initiation of a task to its completion. This includes coding, waiting in queues, and deployment. Shorter lead times allow you to iterate faster on user feedback.
3. Cycle time
measures the time from when work begins on an item until it's fully deployed. Unlike lead time, this metric ignores the backlog wait to give you a pure look at your "execution speed." High-performing teams typically measure this in hours, not days.
4. Change failure rate
This checks the percentage of deployments that trigger a production failure. It's a critical counter-balance to speed. If you're deploying ten times a day but breaking production five times, you aren't agile. You're just reckless.
5. Defect density
This metric tracks the number of bugs per unit of codebase to give you a clear view of system robustness. As AI generates more code, it's important to confirm that bug density isn't rising alongside code volume.
6. Test coverage
This measures how much of your code is executed during automated testing. While 100% coverage is rarely necessary (or efficient), a healthy baseline (70–80%) ensures that refactoring—and AI-generated additions—won't silently break existing functionality.
7. Deployment frequency
How often do you successfully release to production? Elite teams deploy on-demand multiple times per day, using this metric as a proxy for how automated and reliable their deployment pipeline is.
8. Mean time to detect (MTTD)
MTTD measures the time between when an incident actually occurs and when your team first detects it. This is where strong observability practices pay off. If you're finding out about outages from customers instead of from your monitoring tools, this metric will tell you exactly how far behind you are.
9. Mean time to acknowledge (MTTA)
Once an incident is detected, how long before someone actually starts working on it? MTTA reveals gaps in your on-call response and alerting effectiveness. A high number here usually means one of two things: alert fatigue has your team ignoring notifications, or ownership is unclear and people are waiting for someone else to step up
10. Mean time to recovery (MTTR)
When things break (and they will), how fast can you fix them? MTTR measures the average time between detecting an incident and restoring service. It's a crucial metric because fast recovery often matters more than perfect uptime.
11. Technical debt ratio
Technical debt is the "interest" you pay on fast, suboptimal code choices. Tracking this (often through the ratio of rework to new work) gives you the hard data you need to justify "maintenance sprints" to leadership.
12. Sprint burndown
This classic Agile metric tracks the completion of work against time remaining in the sprint. It's your best tool for spotting scope creep or blockers in real time.
13. Pipeline duration (CI/CD speed)
Don't overlook this one. How long does it take for a commit to get through your CI/CD pipeline? If your tests and build process take 45 minutes, your developers are context-switching and losing flow. Optimizing pipeline duration is one of the highest-leverage activities for platform teams.
14. Code review turnaround time
How long does a pull request sit before it gets reviewed? Long review times kill momentum and increase merge conflicts. This metric is often the silent bottleneck in your "Cycle Time" equation.
15. Developer satisfaction (eNPS)
Part of the SPACE framework, this measures how likely your developers are to recommend your organization as a place to work. It correlates strongly with productivity because happy developers simply write better code.
Build the capabilities that drive your metrics
Many engineering orgs tend to set goals like getting MTTR to under 30 minutes, and then stare at the dashboard hoping it magically happens. When it doesn’t happen, leaders just get frustrated and then annoyed at themselves for ever reading blog posts like this one.
DORA research takes a different approach. The framework focuses on building the specific capabilities that actually move the metric forward. For example, if you want to improve MTTR, you need to focus on making it easier for developers to get to the root of the issue and execute the right playbooks.
This might mean a few things, including:
Better observability so teams can pinpoint failures faster.
Clearer ownership so people know who's responsible when something breaks.
Automated rollback workflows so fixes don't require manual intervention.
This is also where tools like Cortex Scorecards become invaluable. Scorecards let you define the specific capabilities (monitoring coverage, ownership clarity, deployment automation) that ladder up to your target metrics. You can track the underlying work that actually moves the needle.
Automating your software development KPIs with Cortex
Tracking these engineering KPIs in spreadsheets works fine when you’re a startup. But as you scale, manual tracking becomes a bottleneck. You end up with stale data, inconsistent definitions, and engineering managers who spend more time updating cells in a Google Sheet than mentoring their teams.
Cortex automates the heavy lifting. By integrating directly with your stack, we provide a real-time, objective view of your engineering health. Whether you're tracking DORA metrics to improve speed or monitoring developer satisfaction to prevent burnout, you get the visibility needed to lead with confidence.
Book a demo today to see how leading engineering organizations are measuring what matters.
