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10 Engineering Metrics Software Engineering Teams Must Follow

This guide covers several robust engineering metrics your team should monitor to ensure product and organizational success.

Is your current cloud cost tool giving you the cost intelligence you need?  Most tools are manual, clunky, and inexact. Discover how CloudZero takes a new  approach to organizing your cloud spend.Click here to learn more.

In software engineering, measuring your performance gives you the knowledge you need to make informed decisions regarding your products, features, processes, and even your dev teams. Measuring also tells you if you’re on track to meet your engineering goals. 

Yet, with an abundance of tasks, data, and other information to keep track of, how do you decide on the right metrics to monitor? How do you know which ones are key to your engineering team’s success — and even the success of your organization as a whole?

To set you on the right path, this guide covers several robust engineering metrics your team should monitor to ensure product and organizational success. We’ll also cover the difference between metrics and KPIs and how to properly measure both. 

Table Of Contents

Engineering Metrics Vs. Engineering KPIs: What’s The Difference?

Engineers, IT professionals, and management use the terms interchangeably. Yet, metrics and KPIs do not mean the same thing.

All KPIs are metrics. But not all metrics are “key” performance indicators in software engineering. 

What are engineering metrics?

Metrics show the status of various aspects of engineering. They include progress or performance data. Today, you can measure plenty of software engineering metrics. They fall into three categories:

  1. Product metrics - These metrics measure various attributes of a product, such as its size, features, design, quality, and reliability. 
  2. Process metrics - These metrics analyze the procedures, tools, methods, and deployment cycles of software engineering.   
  3. Project metrics - These metrics describe the characteristics of a software development project, such as cost per customer, time, people, and customers involved.

Most modern software development tools offer rich dashboards that display a variety of metrics. The different metrics do not contribute equally to improving software quality or engineering workflows. Metrics that correlate with specific organizational objectives are the ones to measure. This is where KPIs come in.

What are engineering KPIs?

KPIs are metrics that tie directly into an organization's strategic goals — and are unique to each business. While engineering metrics focus tactical performance, KPIs are more strategic in that they show whether or not you are on your way to achieving specific engineering goals.

The following are four examples of engineering goals you may be working on:

  • Build more reliable software by reducing the number of bugs reported.
  • Increase deployment cycles by reducing churn.
  • Reduce the length of code to minimize software complexity and errors in lines of code.
  • Rearchitect a specific feature for increased profitability.

Each of these goals will require a metric or set of metrics to measure your progress towards achieving the goal. What KPIs should you measure for each goal? How do you measure the right engineering KPIs for each purpose?

How To Measure The Right Engineering Metrics And KPIs

A lot of engineers feel metrics are unfair. “Software development cannot be quantified”, they might say. However, often they really mean using metrics like "business value", "story points", "tickets", and "lines of code" to judge software engineers' productivity is neither accurate nor fair.   

The following seven characteristics will help you find the right metrics to track and measure. 

  1. You should be able to express metrics in numerical terms.
  2. They should be simple to define, calculate, and understand.
  3. A metric must apply to various phases of the software development process.
  4. Metrics need to be repeatable so that you can measure them consistently. 
  5. Engineering metrics should be cost-effective to calculate. 
  6. Metrics should not be written in a programming language.
  7. Metrics should also be comparable for benchmarking.      

Remember that KPIs are metrics that align with specific organizational goals. Therefore, these characteristics also apply to KPIs. With these attributes in mind, what are the top software engineering KPIs you'll want to track, monitor, and measure for your organization?

10 Core KPIs That Ensure Software Engineering Success

Each organization sets its own KPIs based on its targets, so the following ten won’t make an exhaustive list. However, the following engineering KPIs do span various types of metrics in software engineering, from people and process metrics to project, internal, and external metrics. They include:

  • Quality related KPIs
  • Productivity KPIs for engineers
  • Collaboration KPIs
  • Responsiveness KPIs  

Now let's look at a few of the top KPIs for engineering teams.  

1. Velocity

Engineers or teams use velocity to measure how much work they accomplish in a sprint. It is a performance KPI that is different for every engineer or team. 


Using velocity, you can predict how much time an engineer or team will need to complete a backlog. You can measure velocity in hours or story points. An average velocity of four to seven sprints may be an excellent way to measure it. 

When commits do not match actual work completed, issues are likely present in product throughput, active days, efficiency (percentage of quality code), and commitment size.      

2. Cycle time

Cycle Time Chart

Cycle time is a vital engineering KPI because it measures how much time a task took to complete. It could be a bug fix or adding new code. It also includes each time you reopen work and complete it again. 

You can use cycle times to identify what work takes especially long to complete — and identify bottlenecks that slow down projects. Plus, you can estimate the length of future projects.

To measure cycle time, take the time needed to complete a task minus the start time. The cycle time can be in hours, days, weeks, or sprints. Low values are better. It is usual for cycle time to vary from project to project. However, projects with similar story points should have a consistent average cycle time.     

3. Cumulative flow

Cumulative flow is a progress engineering KPI that shows how tasks are distributed across various stages so you can identify bottlenecks and ensure consistent workflows. 

You measure cumulative flow using a visual chart to show how tickets shift from one status to another over time. For example:

Cumulative Flow

The x-axis represents time and the y-axis represents tasks. The colors indicate the task's status, with red denoting tasks that are awaiting delivery and green showing tasks that have been completed. When the bands progress in parallel, the throughput is stable. This indicates that tasks are entering and leaving your workflow in equal numbers. 

A rapidly widening band means you have fewer tasks completed than cards are coming in. But a rapidly narrowing band shows that you have more capacity than you need. So you can relocate elsewhere. 

4. Sprint burndown

A sprint burndown measures how much work is left to do before a sprint is over. The KPI indicates progress towards a specific goal rather than showing only completed tasks. The sprint burndown chart allows engineers to track the rate at which they must burn down story points to complete a sprint on time. 

The black line below represents that rate: 

Sprint Burndown

The blue line shows how many story points engineers burned down over 10 days. Notice how they couldn't keep up between days three to six but exceeded the target after that. 

This KPI can also help you discover unplanned tasks when you find your work keeps increasing or is stagnant instead of reducing. You can also use it to quickly diagnose and resolve performance problems.   

5. Release burndown

Software engineers use release burndown as a KPI to determine how much work they need to complete before releasing a product. 

It is a powerful metric for estimating releases in real-time. Each time you make a change to the code, it will help you keep track of how it affects the release date. Then, you can predict by how many sprints the release deadline can be shifted.  

It’s like creating a sprint burndown chart, except release burndown measures the entire project's remaining work, whether it's hours, days, weeks, or sprints.      

6. Code churn

Code churn is both an effectiveness and code quality KPI that shows the number of code changes (rewrites or deletions) your developers make within 21 days of writing the first code.

Code churn charts might look this:

Code Churn

A high churn rate during the initial stages of development is healthy, but it should level off towards the release stage. You may have unstable code throughout the various software engineering phases if you have a high churn near release.

7. Mean time to repair (MTTR)

MTTR can be a production or security KPI, depending on the context you use it. 

As a production metric, it measures the amount of time engineers take to repair a problem or recover fully. As a software security KPI, it shows the time it takes engineers to deploy a working solution from the time they discover a security breach.

Calculate MTTR by dividing the total amount of time you spent on repairs over a period of time. A lower number is better.  

MTTR shows how long users must wait before re-using the software in both scenarios. It is what many users refer to as technical support. As a result, it impacts customer experience and how likely a customer will be to recommend your service to others.   

8. Mean time between failures (MTBF)

Engineers and customers use MTBF to gauge software quality and reliability. In actual use, it reflects how long the product lasts without causing any disruptions. The longer the time before repairs, the better.

Divide the total operational time for a given period by the total number of failures to get the mean.  

9. Supporting each other

When you focus on helping customers, you are on the right track. However, collaboration begins closer to home, whether within a team, department, or an entire organization. Teamwork, continuous improvement, and getting team members unstuck are all collaboration KPIs for engineers. 

Metrics you can use to foster a willingness to help each other include:

  • Metrics that show who shares knowledge, such as follow-on commits to comments
  • Comments addressed
  • Reaction time to providing feedback in code review
  • Involvement in PRs

10. Cost

Cost estimation is a key part of resource planning for engineering projects. You can use cost metrics to help you understand your engineers' costs for building and running your products. 

Cost insight can help you in a number of ways, including: 

  • Helping you to set up an adequate engineering budget
  • Supporting engineers to prioritize specific projects over others to prevent overspending 
  • Finding ways to reduce and/or optimize cloud costs 
  • Showing engineers how their actions affect the organization's finances
  • Connecting cloud spend with features, customers, engineering teams, or products

Consider cloud costs, for instance. If you use a cloud service provider like AWS, it can be nearly impossible to see how line items on your AWS bill translate to real business value by simply looking at how much you spent that month. 

Mapping AWS spend to specific features, customers, dev teams, and products transforms this cost data into actionable insights. For example, you can tell which product, feature, or customer is reducing your margins. Moreover, you can easily see exactly what AWS services cost you the most and why.

With a cloud cost intelligence platform, like CloudZero, discovering this cost data and gaining deep visibility into your cloud spend is possible. CloudZero uses machine learning to map cloud spend to your specific products, features, customers, teams, and more.

Take CloudZero’s Cost Per Customer report:

Cost Per Customer

This report allows teams to see how individual customers drive their cloud spend and how specific customers drive their feature costs.

CloudZero also enables engineering teams to drill into cost data from a high-level down to the individual components that drive their cloud spend. 

With detailed cloud cost intelligence, companies can make informed business and engineering decisions (like how to price your products and software) that will ensure profitability. To see CloudZero in action, Request a demo today.


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