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Published:Nov 27, 2023

KPIs for software development: A comprehensive review

If you’re not tracking your KPIs, the best time to start is now.

Tracking software development KPIs can help you improve your workflow, increase productivity, and ultimately lead you to higher customer satisfaction. 

But which KPIs should you prioritize and why? 

In this article, we cover the essential software development KPIs, how to track them, and how to make sure data collection doesn’t become a second job for your team and company. 

What is a software development KPI?

Key Performance Indicators (KPIs) are values used to gauge the overall performance of your business. These metrics provide a clear direction for measuring and evaluating the effectiveness and efficiency of a process or outcome.

The primary purpose of KPIs is to track progress. 

They show you how far you’ve come and how much farther you need to go. Within software development, knowing your progress is crucial for making real-time adjustments.

KPIs show you where you are. But they also shed light on your strengths and weaknesses. These insights help you understand what’s working and what’s not. They help you identify where improvements are needed, ultimately leading to better decision-making.

Making decisions based on gut feelings doesn’t always yield the best results. 

Many businesses inadvertently choose the wrong software development metrics to gauge their team’s success. Metrics like lines of code, fixed bugs, and deploys can be remarkably misleading. They often fail to align with real business objectives.

The reason behind this common misstep is simple: business managers typically lack the intricate understanding of software development required to define a successful project.

But, what are these metrics? How do they marry the interests of business and software quality?

The three types of software development KPIs

To help you better understand the software development KPIs you need to monitor, we’ve categorized them into three types, starting from the most general to the most specific metrics.

Type 1: KPIs that track ‌overall performance 

Performance metrics track the progress of specific tasks or projects and assess your team’s overall performance. By focusing on them, you can keep a clear view of project progress and how well the team is progressing towards their goals and meeting their performance targets.

Here are the four essential metrics to keep track of in this category: velocity, burndown chart, release burndown, and cumulative flow.

Type 2: KPIs that track efficiency and workflow 

These metrics focus on how efficiently work is being done within the software development process. They measure how well your development team manages its work processes and strives for optimal performance. 

These metrics directly affect speed, quality, cost, and overall value. They can help you identify bottlenecks, streamline processes, and ensure that your team operates efficiently.

In this category, we’ll go over these five KPIs: Lead time, cycle time, flow efficiency, wasted effort, and interruptions.

Type 3: KPIs that track code quality 

As the name suggests, code quality KPIs assess how well your development team produces high-quality, reliable code that’s well above your industry standards and user expectations. 

There are the four key metrics we’ll focus on here: Code coverage, code stability, code simplicity, and code churn. 

With that in mind, let’s dive into the first type of KPIs.

Performance KPIs for software teams

"performance KPIs in software development displaying the formulas for velocity, sprint burndown, release burndown, and cumulative flow

Starting with ‌performance KPIs, these metrics are relevant whether the team works in DevOps or not.

Collecting performance KPIs is an essential step towards learning about your team’s overall productivity and progress. 

1. Velocity

Velocity is a numerical representation of your team’s pace. It illustrates how much work your team can complete in a given time period (usually measured in story points or tasks).

Story points are units of measure for quantifying the amount of work involved in a project, including the number of tasks and the effort needed for completion.

In Agile, a sprint is a dedicated period of time in which a set amount of work will be completed on a project.

velocity chart displaying four sprints showing commitment and delivered  work items per sprint

To measure it, we just divide the total story points finished by the number of sprints. 

How to measure it: Velocity = [Total story points completed] / [Number of sprints]

Example: Here’s an example to illustrate the formula:

Let’s say you have an Agile development team that’s completed the following number of story points in their past sprints:

  • Sprint 1: 20 story points
  • Sprint 2: 25 story points
  • Sprint 3: 18 story points
  • Sprint 4: 22 story points

To calculate the velocity for this engineering team, you’d sum up the total story points completed:

Total Story Points Completed: 20 + 25 + 18 + 22 = 85 story points

Now, you need to find the number of sprints:

Number of Sprints = 4 sprints

Finally, you can calculate the velocity using the formula:

Velocity = 85 story points / 4 sprints
Velocity = 21.25 story points per sprint

2. Sprint burndown

Sprint Burndown is a valuable tool for Agile development teams to measure their progress and ensure they stay on track with their sprint goals. 

It provides a visual representation of the work completed compared to what was initially planned, using story points as a measure.

The burndown chart helps you identify any deviations from the initial sprint plan. If the team is falling behind, it becomes evident, so you can make timely adjustments to get back on track. This helps ensure that the team can deliver the planned work within the sprint timeframe.

burndown chart showing remaining effect starting at 120 and burning down to 0 on day 20

How to measure it: Sprint Burndown = [Initial story points] – [completed story points]

Example: Suppose your team begins a sprint with 40 story points planned, and after two days, you’ve completed 10 story points. Your Sprint Burndown would be:

Sprint Burndown = 40 (Initial) – 10 (Completed) = 30

This means 30 story points are remaining to be completed by the end of the sprint.

3. Release burndown

Release burndown is like the big brother of sprint burndown. While sprint burndown charts focus on the progress of individual sprints, release burndown takes a step back to encompass the entire release cycle of the product.

It shows project managers, developers, and stakeholders whether they’re behind on time, or ahead of product schedules.

release burn down chart graph showing story points starting at 120 and moving between behind schedule to ahead of schedule and burning down to 0 on sprint 7

For businesses, release burndown charts provide hard data to communicate with all stakeholders. 

You can let your customers know about any changes in the release schedule and your shareholders about when they can expect a return on investment post-release. 

This transparency builds trust and aligns the expectations of all ‌stakeholders.

How to measure it: Release burndown = [Total planned story points] – [Total completed story points]

Example: Let’s say you’re working on a software project with a total planned story point count of 100. At the end of Sprint 1, the team completes 30 story points, and by Sprint 2, they’ve achieved 60 story points. By the end of the project, they’d have completed a total of 90 story points.

Using the formula, your Release Burndown would look like this:

Release burndown = 100 (Total planned) – 90 (Total completed) = 10

You have a release burndown of 10, indicating that 10 story points are remaining to be completed by the end of the project.

4. Cumulative flow

Cumulative flow shows you what stage your work items are in. 

It provides a visual representation of the different stages that work items go through, such as backlog, to-do, in-progress, and done. Each stage is represented by a different color, making it easy to see where work items are in the cycle.

cumulative flow diagram showing the spread of development steps month over and month, specifically how a bottleneck can be identified

This helps teams identify bottlenecks, delays, patterns in their workflows, and helps in optimizing resource allocation and utilization.

How to measure it: Cumulative flow = [Work items in backlog] + [Work items in progress]+ [Work items done] and any other items you have in your workflow stages. 

Workflow and efficiency KPIs for software development

These KPIs identify how efficient ‌your workflow and processes‌ are. They help you monitor whether changes should be made within the whole development process or just certain parts of it. 

5. Lead time

Lead time measures the average time it takes to go from the moment a new task is created to its delivery. In other words, it’s the total time between the moment that your software team members agreed to take on a user story or a project to the time it was delivered. 

Often, software teams use lead time and cycle time interchangeably, but they aren’t the same. 

Lead time measures the entire journey from the customer’s request to the final delivery, as we’ve discussed. 

On the other hand, cycle time focuses on the time it takes to complete the work item once you start working on it. Think of it as the “hands-on” time without considering the waiting time in the queue.

graph showing lead time measured from task created to work completed while cycle time measures time between two consecutive tasks

We’ll speak about cycle time in detail next, so let’s focus on how to calculate ‌lead time.

How to measure it: Lead time = [Delivery date] – [Request date]

Example: if a customer requested a feature on January 1, and it was delivered on January 15, the lead time for that feature would be 15 – 1 = 14 days.

6. Cycle time

Cycle Time is the average time it takes to complete a work item. It measures the average time it takes to complete a work item, from start to delivery. By tracking cycle time, teams can assess how quickly they are completing tasks and user stories.

How to measure it: Cycle time = [Net production time] / [Number of units produce]

  • Net production time: This is the total time spent on producing the units, minus any idle time or delays. It’s the actual hands-on, get-down-to-work time.
  • Number of units produced: This is the number of items or units that were successfully produced within the given time frame.

Example: Let’s put this formula to the test with a real-world example. Imagine we’re managing a manufacturing process that produces 1,000 widgets in 40 hours of net production time.

Using our formula:

Cycle Time = 40 hours / 1,000 widgets = 0.04 hours per widget

So, our Cycle Time is 0.04 hours (or around 2.4 minutes) per widget 

7. Flow efficiency

Flow efficiency measures the percentage of time that work items spend in active work states compared to waiting states. 

A lot of times, work-in-progress just means that tasks are not moving efficiently through the different stages. Which increases the waiting time that developers have to deal with in between tasks. 

Flow efficiency metrics help you solve that by calculating low-efficiency periods. By identifying the causes, you can take steps to remove those inefficiencies. 

How to measure it: Flow efficiency (%) = [Total active time / Total lead time] * 100

Example: Let’s say we’re working on a software development project, and we want to calculate the flow efficiency for a particular task. Here’s how we can do it:

  • Total Active Time: The time the team spent actively working on the task is 20 hours.
  • Total Lead Time: The total time from the task’s initiation to completion is 30 hours.

Using the formula: Flow efficiency (%) = (20 hours / 30 hours) * 100 = 66.67%

In this example, our flow efficiency for the task is approximately 66.67%.

8. Wasted effort metric

The wasted effort metric measures the percentage of time or effort spent on activities that add little or no value to the project. These activities can range from rework and defects to over-engineering. 

If you’re struggling with feature prioritization, the wasted effort metric solves this issue. 

By monitoring waste, you can prioritize working on new features and product development, ultimately increasing customer satisfaction and retention. 

How to measure it: Wasted Effort Metric = [Total wasted time] / Total productive time] x 100

For example, if a project had a total wasted time of 100 hours and a total productive time of 500 hours, the wasted effort metric would be:

(100 / 500) x 100 = 20%

In this scenario, the wasted effort metric indicates that 20% of the project’s time or effort was spent on non-value-adding activities.

You can also use value stream mapping to identify non-value-added activities and clearly visualize wasted efforts in your workflow. 

9. Interruptions time

In our context, interruptions refer to the unplanned or unexpected events and conditions that disrupt or divert our attention from our work tasks.

A study of four Agile software development teams found that they experienced an average of 9.5 interruptions per day, with an average duration of 8.4 minutes.

graph showing lead time measured from task created to work completed while cycle time measures time between two consecutive tasks

These interruptions can be grouped into three main categories, each with its unique impact:

1. Developer’s team productivity: These interruptions affect the efficiency and output of the development team. They can include impromptu meetings, sudden priority shifts, or even technical glitches that demand immediate attention.

2. Software performance: These are issues that impact the performance of the software in development. These could be sudden crashes, performance bottlenecks, or compatibility problems.

3. Defects and usability: Interruptions can also come in the form of defects or usability issues that need fixing. These issues can range from minor bugs to critical functionality breakdowns.

These can all be accounted for as interruptions.

How to measure it: Interruption impact = [Interruption frequency] × [Interruption duration]  / Total work time

Example: Let’s say your Agile software team experiences interruptions, on average, three times a day, with each interruption lasting almost 15 minutes.

If your total work time in a day is 8 hours (480 minutes), the interruption impact would be:

Interruption impact = [3 interruptions] × [15 minutes] / 480 minutes = 11.25%

So our total interruption rate for 8 hours is 11.25%.  

Code quality KPIs in software development

These are specific to the quality of coding and development skills of your team. They help you track the ‌speed, time, and quality of the code that the team is producing.

Code quality KPIs for software development teams

10. Code coverage

Code coverage enables development teams to assess the quality of their code. It measures the percentage of your source code that gets executed by automated tests. 

Code coverage plays the role of a quality assurance manager, helping you improve code quality, and ensuring that your code performs as expected. This reduces the risk of sneaky bugs or unexpected errors creeping into your codebase.

This means less debugging for your team and more time to focus on more important tasks. 

How to measure it: [Number of lines or branches of code tested] / [Total number of lines of code] * 100

Example: To illustrate, let’s say you have a module with 100 lines of code, and your tests cover 80 of those lines. Here’s how you’d calculate ‌code coverage:

(80 lines covered / 100 total lines) * 100% = 80% code coverage

You don’t need your code coverage to be 100%, but the higher it is, the fewer bugs you’ll have to deal with. 

11. Code stability

Stable code implies that there are minimal changes to the software. This helps keep your product stable, increases its longevity, and reduces technical debt. Which is the accumulated cost of shortcuts taken during development. 

It also measures the ease with which your source code can be modified, updated, or extended to meet changing requirements or fix issues.

How to measure it: The tricky part is code stability is hard to measure. One method is to use the maintainability index

This index, typically available in development tools, quantifies the maintainability of your codebase. It considers factors like code complexity, code duplication, and code size. The higher the index, the more maintainable your code is.

Example: Suppose you’re working on a software project, and your Maintainability Index is consistently above 80 on a scale of 0 to 100. That’s a sign of a highly stable codebase. Your team can easily modify, update, or extend the code without breaking a sweat.

12. Code simplicity

Code simplicity measures the degree of difficulty or intricacy embedded within the source code. 

How to measure it: There are multiple ways of measuring it but the most straightforward method is ‌cyclomatic complexity

This metric quantifies the number of independent paths in a codebase. The lower the number of the paths in the codebase, the simpler it is. The formula may look intimidating, but its application is straightforward:

M = E – N + 2P

Where:

  • M is the cyclomatic complexity.
  • E is the number of edges in the control flow graph.
  • N is the number of nodes in the control flow graph.
  • P is the number of connected components.

While using quantitative metrics like cyclomatic complexity, you can also use qualitative assessments to add a human touch to code complexity evaluation. You can do manual code reviews, where developers collectively review code and assess its simplicity or complexity based on factors like naming conventions, code structure, and readability.

Simpler code is easier to understand and test, and takes less time and money to maintain.

13. Code churn

Code churn‌ measures the percentage of code that undergoes significant changes during a time period. The changes can be rewrites, updates, or deletions of code.

graphic representation of code churn, a bar graph split on an x axis with "productive" measurements above the line and churn measurements below the line

It’s the software’s way of saying, “Wait, let me try that again.”

This can ‌also be a measure of code stability and quality. 

Testing code churn involves assessing the codebase and measuring changes over time. One common method is calculating the rework percentage, which quantifies the proportion of code that requires modification after its initial creation. 

Another valuable metric is ‌defect percentage, indicating how many code changes are due to bugs or issues.

How to measure it: Code Churn = (Lines added) + (Lines modified) + (Lines deleted) in [Time period]

Example: Suppose you’re working on a software project for over a week, and you want to measure the code churn for that week. Here are the statistics:

  • Lines Added: 200 lines of code
  • Lines Modified: 150 lines of code
  • Lines Deleted: 100 lines of code

Using the formula, you can calculate the code churn for the week:

Code Churn = (200 lines added) + (150 lines modified) + (100 lines deleted) in [1 week]

Code Churn = 450 lines of code in 1 week. 

Avoiding data overload 

While KPIs are invaluable for gauging progress and making informed decisions, there’s a fine line between insightful data and information overload. 

Collecting too many metrics can become a second job for your team, leading to over-reporting — a situation where actionable insights are buried under piles of data.

To strike the right balance, it’s crucial to not only focus on KPIs that align with your business goals and software quality standards but also to use developer-friendly apps like 7pace.  
With 7pace, there’s no need for manual data gathering and analysis. The platform offers natively integrated reporting experiences and automatically categorizes each developer’s effort concerning design, development, deployment, and testing.

Feel free to download our free guide to hassle-free reporting to learn more about how 7pace can make data reporting a seamless experience.

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