Software development is a complex process with many technical aspects, evolving client requirements, and constant close teamwork. With so many moving parts, maintaining alignment and efficiency can be difficult, especially as remote work becomes more common across the industry. The question is: how can you ensure your team is working effectively and moving in the right direction?
That’s where key performance indicators (KPIs) come in. By setting key performance indicators (KPIs) and analyzing them over time, you can ensure your software development team stays on track, reduce errors and bottlenecks in the software development process, and even increase efficiency.
Below, we will discuss the top key performance indicators for software development teams and how to measure them effectively.
What Are KPIs and Why Is Tracking Them Important?
Key performance indicators (KPIs) are measurable metrics that help evaluate how effective your team’s or company’s actions are in reaching specific goals. Evaluating and monitoring them over a given period of time helps assess the effectiveness and potential areas for improvement of specific employees, business processes, and the company as a whole.
Don’t confuse KPIs with raw reports that project managers review in project management tools like Jira to understand how the project is going. For instance, such reports as Velocity Chart, Burndown, or Cumulative Flow Diagram. They are monitoring tools that PMs use to manage the process on a daily basis and don't always measure results. A KPI is a calculated percentage or ratio derived from such reports. They provide objective benchmarks towards a specific business goal (e.g., % of tasks completed on time, average task completion time) that help you understand if your workflow is truly effective.
They can be used to judge whether the team is coping with tasks, whether the process is stable, or if any intervention is needed. Besides, when teams track KPIs and some problems arise, it becomes much easier to identify and take required corrective actions with respect to inefficiencies and bottlenecks in workflows. You can also use them for predicting the future— planning sprints, evaluating resources, and forecasting deadlines and risks. For remote teams, KPIs additionally promote structure and transparency in workflows, ensuring they can achieve accountability and collaboration across time zones.
What Are the Top KPIs in Software Development?
Team Average Velocity
Most software development companies use Scrum as a method for organizing product development, dividing the process into fixed time intervals called sprints. Each sprint is typically focused on completing specific tasks called user stories. Your team’s velocity is how much work it can complete during one sprint, i.e., your team’s productivity per iteration. The most popular way to measure velocity is story points. For instance, completing task A takes 3 story points, while completing task B takes 5 story points. Estimates for each sprint are subjective, so it is recommended to evaluate data from at least three sprints for a more accurate result. If velocity remains the same over several sprints, you may need to consider other factors, such as team capacity or task complexity, when making predictions. Knowing the total number of story points for your project will help you estimate the approximate time it will take to build and deploy the product.
How to measure: Sum story points for completed user stories in a sprint and divide this number by the number of sprints. Track over 3 sprints to establish a baseline. For instance, if in the first spring your team completed 30 story points, in the second sprint - 28 story points, and in the 3rd sprint - 32 story points, then your team’s average velocity is (30 + 28 + 32) ÷ 3 = 30 story points per sprint. This tells the team they can realistically take on about 30 story points in the next sprint.
Sprint Completion Rate
Sprint Сompletion Rate is a specific KPI that measures how much work has been actually completed during a sprint. It differs from velocity, which estimates how much work the team can complete on average over multiple sprints. This KPI shows the team's ability to complete planned tasks. By analysing Completion Rates for different sprints, you can determine the average rate. If the rate is less than 100%, it may indicate that the team plans too many tasks or underestimates their complexity. The team can adjust the velocity or scope of future sprints to ensure all tasks are completed.
How to measure: Sum all tasks that were planned at the beginning of the sprint (user stories, bugs, technical tasks). Then divide tasks with a status of "Done" or equivalent in the Jira/PM system at the end of the sprint by the sum of all planned tasks. For example, if 20 tasks were planned and 16 completed, then this particular sprint completion rate is 80%.
Average Cycle Time
Cycle Time is a software KPI that reflects the time it takes for a task to move from start to completion. Measuring cycle time can have many benefits. First, you have a metric that can objectively assess your team's productivity. It also gives you an estimate of how quickly your team will complete future tasks. Similarly, you can identify any inconsistencies, such as obstacles to a rapid workflow. You can also summarize all development cycles over a given period and compare them with other similar data to better understand the quality of your team's work. Tasks can be divided by type, like bugs or features, to understand bottlenecks.
How to measure: Calculate the average cycle time by dividing the sum of cycle times of all tasks by the total number of tasks. You can use tools like Jira, GitHub, or Linear to track the cycle time of each task from the moment it is started until it is marked as done. For instance, Task A starts on Oct 1 and is completed on Oct 4, which means the cycle time is 3 days. Task B starts on Oct 5 and is completed on Oct 10, then the cycle time is 5 days. Task C starts on Oct 11 and is completed on Oct 15, so its cycle time is 4 days. The average cycle time is then 4 days per task (3 + 5 + 4) ÷ 3).
Flow Efficiency
Flow efficiency is a KPI that measures the proportion of time spent on solving a task actively (without downtime and waiting) relative to the total task execution time. For instance, when solving a task, there may be waiting periods when developers can't immediately move from one task or project to another (e.g., waiting for requirements, code review, or external input). Sometimes code has many dependencies, and you can't start working on one until another is finished. A high percentage of Flow Efficiency means the team has less downtime, so processes are efficient. Low percentage means there are lots of waiting, blockages, bottlenecks, and that the workflow needs to be optimized. Flow Efficiency helps attribute poor efficiency to specific time periods to better understand the underlying causes and make corresponding changes to project management. It can also provide insight into the distribution of tasks across different waiting periods. It can also be useful for tracking how long you're waiting for another team member to finish work.
How to measure: You can calculate flow efficiency by dividing how much time you actively spend on work by your total cycle time. For instance, a task took 10 days total, but only 4 days were active work, so flow efficiency was 40%. It is similar to the coefficient of performance (COP) index. Tools like Jira or Azure DevOps can show the time a task spends in active statuses like “In Progress”, “Code Review”, or “Testing/QA” vs in inactive statuses like “To Do / Backlog”, “Blocked / Waiting”, or “On Hold”.
Code Stability
Code stability is crucial before a product release. Code stability means that few changes are made to a software product that could potentially harm the business or the software. Stable code means fewer bugs, less downtime, and less rework. Measuring frequency of code changes alone doesn’t directly show stability — frequent changes aren’t necessarily unstable if they don’t cause problems. Measuring the percentage of changes that cause downtime or errors is a more precise way to define code stability. The higher stability percentage will mean the code is reliable and low-risk. A lower stability percentage means the code may need refactoring or better testing.
How to measure: Code stability can be calculated using different methods:
By regressions or bugs after changes: count how many modules or functions did not cause regressions or bugs after changes, and divide by the total number of modules. For instance, if you have a module with 10 functions. After a new feature was added, 8 functions worked fine, and 2 caused bugs that had to be fixed. It means 80% of your code remained stable and 20% required rework.
By code changes and their consequences (error-free changes): count all changes (commits, pull requests) and divide by those that did not cause bugs or errors. For instance, if during the week, your team made 50 commits across multiple features, 40 commits did not cause bugs or regressions, while 10 commits caused new bugs or issues, it means 80% of the changes were error-free.
Combined approach using analysis tools: tools like SonarQube, Code Climate, Bug Tracking, or Jira can monitor bugs, code smells, and regressions over a period and convert this into a stability percentage.
Code Test Coverage
Code coverage is a software development KPI that teams use to define code quality. It measures how many lines of code are executed during testing. Code that isn't executed for some reason likely contains undetected errors. While it is not necessary to strive for 100% code coverage, the higher the code coverage, the better and the less debugging you'll have to do. Comprehensive test coverage does indicate that you've prioritized the codebase and identified the features that are most important for the project's development. This specific metric is crucial for software development lifecycles that prioritize continuous delivery and test-driven development (TDD).
How to measure: To calculate Code Test Coverage, you need to divide lines, functions, or branches of your code executed by tests by the total lines of code. For instance, 800 lines executed by 1000 total lines, which means you have 80% coverage. Tools like Jest, SonarQube, Istanbul, pytest-cov, JaCoCo, etc (depending on the language of code) can help you measure which lines, functions, or branches of your code are executed during tests.
Code Simplicity
Code simplicity is a KPI that helps you see how hard your code is to test and fix later. It can be measured using various metrics. One such metric is cyclomatic complexity, a quantitative measure of the number of independent paths your code must traverse. Fewer paths are a good sign. Generally, simpler code is easier to test and maintain, reducing the risks of long-term onboarding for new developers and technical debt. To access code complexity, use the following rating scale:
1–10: Simple code with low risk
11–20: Moderately complex, requiring more testing
21–50: High complexity, difficult to maintain
50+: Extremely complex and nearly untestable.
If your functions consistently score above 20, you might need to simplify your logic, break large functions into smaller ones, or refactor them to improve code readability. Other relevant metrics include the Maintainability Index (which combines complexity, lines of code, and comments) and Code Smells (potential design or readability issues such as repetitions, long methods, or deeply nested conditions).
How to measure: Tools like SonarQube, CodeClimate, ESLint Tools, or Radon automatically calculate the complexity for each function. But don’t average all the numbers because one huge messy function can make the average look bad, even if most of the code is simple. Use median complexity: put all function scores in order and pick the one in the middle. For instance, among 50 functions, the middle one is 3, so most of the code is clean.
What KPI should you track and how to track them effectively?
Picking KPIs for the software development industry isn’t about tracking everything, it’s about tracking what really matters. Here’s what to focus on when picking the right KPIs.
Think team-first: Developers usually work in teams, and their work may depend on the work of other team members, which can make it difficult to evaluate performance using individual KPIs. That’s why tracking team-level metrics is often more meaningful, they reflect collective productivity and collaboration rather than isolated individual output.
Value quality over numbers: Some common metrics, like lines of code, commits, or deployments, don’t always reflect a team’s true progress or goals. Developers’ work is quite creative, so a similar task can have different levels of complexity for different projects. That’s why putting it into a rigid framework is like telling an artist they must create a certain number of paintings per month. When setting KPIs in software development, they should be not about how much code you write, but about how well it works and how happy users are.
Match KPIs to your project: The KPIs we provide above are just a part of what you can actually track, so it is important to choose KPIs for your goals, tools, and company culture. For example, teams practicing DevOps often use DORA metrics (Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Restore) to track delivery performance and system reliability KPIs. They show how efficiently and safely code moves from development to production.
Get everyone on board: Don’t just set KPIs at the management level, involve developers, and QAs in defining them. Explain why each KPI matters and how it helps improve their daily work. When people feel included, they’re more likely to take ownership, act on the insights, and care about the results.
Start small: Begin with a few key metrics. Make sure every KPI follows the SMART principle. It means they should be specific, measurable, achievable, relevant, and time-bound. Once they work well, expand gradually.
In conclusion
A KPI, in its simplest form, is a performance measurement method that helps you understand how your development team or business is performing. Organizations use them to monitor performance, identify problems, and ensure accountability. KPIs increase employee motivation, improve decision-making, and align goals with strategy. To begin with, focus on KPIs that align with your goals and reflect your team’s specific workflow and tools. Start small, measure consistently, and adjust as you learn what truly drives progress. When used correctly, a good KPI should serve as a compass, indicating whether you're moving toward your goals.
FAQ
How many KPIs should you track?
Start small — around 3–5 KPIs that directly align with your goals. Tracking too many KPIs can add noise and make it hard to react to insights they offer. Once the process stabilizes, you can add more.
How often should I review selected KPIs?
Ideally, you should review your KPIs at the end of every sprint or once a month. Reviewing them on a regular basis will help you identify trends and react before real problems occur.
What’s the difference between a KPI and a metric?
A KPI is a key indicator that is derived from a metric. A metric is a snapshot of raw data. A KPI shows you how close or far away you are from your objective (e.g., sprint completion rate, percentage of code stability).
What tools can help automatically track software KPIs?
Tools like Jira, GitHub Insights, GitLab, SonarQube, CodeClimate, Linear, or devActivity can help you collect data and visualize key metrics like your velocity, code coverage, or cycle time.
How do I know if my KPIs are effective?
Effective KPIs are SMART — Specific, Measurable, Achievable, Relevant, and Time-bound. If they help you make informed decisions or improve your workflow, they’re working.
Should remote teams use different KPIs?
The KPIs are basically the same, but remote teams should adapt more toward KPIs around transparency, communication, and workflow efficiency. For example, tracking cycle time or flow efficiency can become even more important.
What’s a good KPI benchmark for code quality?
While benchmarks vary, aim for at least 80% code stability, 70–90% code coverage by tests, and median cyclomatic complexity below 10.
How can KPIs improve team motivation?
KPIs make progress visible. When developers can see the results of their work in a measurable way, for example fewer bugs, faster releases, or increased performance, it instills a sense of purpose and accountability.
What should I do if a KPI drops?
Don’t panic. A KPI decrease indicates a signal, not a failure. Determining a cause should involve team members participation if possible. Use the decrease to decide how to alter the process. The goal is to improve continuously, rather than just be perfect.
What are DORA metrics?
DORA metrics (Deployment Frequency, Lead Time for Changes, Change Failure Rate, Mean Time to Restore) are DevOps metrics that often become KPIs when tied to business or delivery goals, such as improving deployment speed or system reliability.
