Let's face it: measuring software development is challenging. Throwing engineering KPIs at your team won't magically solve problems like poor code quality or lagging productivity. Metric misuse can even foster resentment and can easily introduce dark patterns.
But here's the good news: Effective KPIs, implemented thoughtfully, can be a game-changer. When used strategically, they transform from mere numbers into powerful tools for leaders. These tools can not only showcase areas for improvement, but also empower you to advocate for your team's needs with data-informed insights.
In this article, we'll examine 21 engineering KPIs, such as scope creep and code quality, and how to leverage them effectively. We'll investigate why engineering KPIs matter and the different types of engineering KPIs to explore.
Table of contents:
- What is a software engineering KPI?
- Types of engineering KPIs
- 21 software engineering KPIs
- Why do engineering KPIs matter?
- How should you choose engineering KPIs?
- FAQ
What is a software engineering KPI?
Software engineering KPIs (key performance indicators) are measurable values optimized for development teams. KPIs allow organizations and businesses to see how metrics change over time, enabling them to optimize processes for increased efficiency.
Software engineering KPIs focus on metrics that apply to development, such as software quality, production speed, and overall developer well-being. We'll explore different engineering KPIs available, ranging from quantitative to qualitative indicators.
By exploring software development KPIs and establishing a consistent process, your team can identify and integrate the most beneficial software engineering KPIs into your development cycles for improved outcomes.
Metrics vs. KPIs: What's the difference?
People routinely use metrics and KPIs interchangeably, but they are different. Metrics are measurements, such as how long it takes for a code cycle to complete or the number of bug reports after an update goes live. KPIs are metrics tied to a goal or objective.
Consider the number of bugs reported in the scenario above. This is a metric, a foundational piece of data. When we use this metric to gain insights into quality control and how a team is progressing toward more stable releases, it transforms into a KPI, a metric with a time-bound, agreed-upon, strategic purpose.
While metrics provide information, KPIs are goal-oriented and allow for actionable decision-making. For example, teams can interpret a software development KPI, such as the bug example shared above, and decide that additional QA testing may be necessary before a release.
Types of engineering KPIs
Engineering teams can choose from different types of KPIs to measure performance and improve overall efficiency. The heart of any KPI is the metrics, or indicators, that provide data. We'll explore the different types of KPIs by looking at specific indicators.
Quantitative indicators
Analysts can identify most metrics as either quantitative or qualitative indicators. Quantitative indicators can be expressed numerically. If you're collecting data represented by numerical figures, it's quantitative.
For example, the number of commits deployed by a team within a set period or the time taken for development cycles are quantitative indicators.
Qualitative indicators
Qualitative indicators are metrics not defined by numbers. They may express emotions or experiences, or they may be a collection of comments that don't translate into digits. Qualitative indicators are common when working with feedback or non-structured surveys.
For example, if you collect user input on your latest software release, you may offer a text entry field for testers to include feedback—this is qualitative, as numbers can't represent it.
Leading indicators
Leading indicators look toward the future and help predict results based on current trends and data. Using such indicators can be helpful when attempting to understand potential future problems or successes. They can also be powerful tools enabling management to be more proactive, adapting to changes, and mitigating risk with greater ease.
You could focus on scope creep and time to merge KPIs, which reveal whether a project is lagging. These KPIs act as leading indicators, allowing project leaders to predict whether a development team will achieve estimated ship dates.
Lagging indicators
On the flip side, lagging indicators are metrics focused on the past. Using lagging indicators, teams can review past implementations and measure their effect. Comprehending how past measures affect progress toward a goal is critical; it allows teams to make course adjustments for future actions.
For example, a lagging indicator could be the number of support tickets filled after previous pushes. By looking back and examining this metric, teams can better understand if their actions caused more trouble or benefit for users and make necessary adjustments.
Input indicators
Input indicators help categorize the KPIs available. Engineers typically use these indicators to track the progress of a project or task; in software development, they can help track a development cycle. Input indicators are focused on the input or resources used to drive a project to completion.
For example, input indicators may be the number of developers on a project, the time it took to complete, or the budget allocated to the team. One KPI that takes advantage of input indicators is budget variance, which examines the amount of money spent on a project compared to the budget.
21 Software engineering KPIs
Software engineering KPIs allow for a better understanding of a project's trajectory and help push for increased efficiency. By utilizing KPIs to comprehend task positioning, teams can adjust their actions to better align with their organization's goals.
We'll examine 21 software engineering KPIs, from project completion rates to scope creep and customer satisfaction scores. There are an overwhelming number of KPIs out there, but starting with these options is a sure way to dip your development team's toes into the proverbial pool of KPIs and focus on efficiency.
1. Project completion rate
The project completion rate KPI highlights the number of projects completed on time; it may be represented as a percent or the exact number of projects completed. This KPI is useful for project planning, enabling team leaders to better schedule time for future projects and allocate resources with more precision.
2. Schedule variance
The schedule variance KPI enables teams to estimate whether a project is on track with a predetermined timetable. To obtain a variance, subtract the completed time from the predicted time. For example, the variance is two days if a project is due on April 2, but the team finishes it on April 4.
3. Scope creep
Scope creep occurs when a team adds new features to a project or alters established requirements on a project already underway. By learning to manage scope creep, decision-makers can comprehend how well they prepare project requests and how they can better allocate resources to projects for the future.
4. Pull request size
Larger pull requests can introduce complexity and are often more challenging to review. These factors can slow development or introduce bugs into your production environment. Understanding pull request patterns can help identify how to mitigate risks and ensure collaboration benefits your process rather than harms it.
5. Developer experience
The developer experience KPI is a critical metric that informs project leaders of overall developer well-being. Team leads can utilize the Developer Thriving Framework to improve developer experience, improving employee retention and heightened productivity while increasing team collaboration. As such, tracking developer experience metrics within your organization is critical to fostering a better working environment for all team members.
6. Return on investment (ROI)
ROI allows companies to understand the amount of return yielded from an investment. Calculate ROI by dividing the net return on your investment by its original investment value. Engineering leaders can utilize ROI to better understand the resources, including time, developers employed, and money spent on a project compared to its resulting financial value. From a business perspective, this helps paint a picture denoting the project's effectiveness and overall success.
7. Deployment frequency
Deployment frequency is a powerful DORA metric and KPI that showcases how often a team successfully deploys code. Developers can calculate the KPI by dividing the number of code deployments within a set period by the number of weeks. Tracking deployment frequency and other DORA metrics can help your team better understand the software delivery process and identify areas for improvement.
8. Mean time to recovery (MTTR)
The mean time to recovery KPI, sometimes known as the time to restore service, is a DORA metric focused on the average time it takes to recover from a system or product failure. By focusing on decreasing the time to recovery, you can reduce the amount of time customers spend without your product, heightening customer satisfaction.
9. Change failure rate
Change failure rate is a DORA metric that notes how often team code deployments have incidents or failures attached to them. By comprehending when failure rates occur, teams can examine their QA process to decrease faults. You can leverage your change failure rate with Pluralsight Flow’s DORA measurement tools to identify if automated testing is the right solution for reducing production time and increasing reliability.
10. Customer satisfaction score
Customer satisfaction score is one of the most crucial KPIs and a paramount lagging indicator. As the name suggests, this KPI alerts you to your users' satisfaction level. A common way to determine customer satisfaction scores is to use the Net Promoter Score (NPS) method, which you can learn about in the Exploring Customer Support course.
11. Code quality
Code quality is an indicator of your software's performance. It builds upon other engineering KPI metrics, such as reliability, to paint a better overall picture. To establish a code quality KPI, your team must determine what metrics you want to measure against. For example, a team could create a code quality KPI by examining reliability or change failure rates.
12. Lead time for changes
The lead time for changes KPI is a DORA metric that spotlights the time it takes for committed code to enter production. By better understanding the lead time for changes, teams can identify ways to improve their development cycle, like introducing new processes and methodologies. To calculate lead time for changes, subtract the date/time of a commit from its date/time of employment.
13. Code review velocity
Code review velocity is a KPI that highlights the time it takes (also known as the speed) for code changes to be reviewed and merged into the codebase. Developers can focus on smaller, more frequent code commits and integrate automated quality checks to increase efficiency. Consider utilizing a code review checklist to keep your process consistent and streamlined.
14. Throughput
Throughput is a coding metric connected to the amount of code contributed within a set period. Teams can break this metric down into raw throughput, which reports all code contributed, and productive throughput, which focuses on the net lines of code after a rework. Raw throughput gives teams a better idea of how much code developers produce, while productive throughput provides insight into how long contributed code stays around before being modified.
15. Developer satisfaction
Developer satisfaction is one KPI that contributes to the overall developer experience. Keep your developers thriving by tracking metrics that could impact them, such as the types of code work leaders assign them. Conduct regular satisfaction surveys to collect data surrounding developer opinions on current toolsets, development process, overall team dynamics, and even leadership.
16. Team collaboration
Team collaboration is at the heart of every software development unit. Turn team collaboration into a valuable KPI by examining how well your developers contribute to pull requests. Identify submitter metrics such as response timeliness or look closer at how developers provide feedback and when it leads to actionable code changes.
17. Investment distribution
The investment distribution KPI is a metric team leaders use to comprehend how much time developers utilize across tasks. Tools like Pluralsight Flow offer investment profiles that use an algorithm to showcase where developers spend their time, helping companies calculate costs and confidently tackle their roadmap.
18. Sprint/release burndown
Sprints are an integral part of the software development lifecycle. Sprint and release burndown KPIs give teams a more concrete conceptualization of the amount of work left within a sprint relative to the remaining time. Understanding sprint and release burndown times within Agile development allows developers to plan more efficiently, leading to better project completion rates.
19. Story points completed
Story points completed is a KPI that examines the total number of story points developers completed during a coding sprint. It can be useful when determining other engineering KPI metrics. Within Pluralsight Flow, the sprint movement report's completion percentage is calculated by dividing completed story points by the total number of story points within a sprint.
20. Time to merge
Time to merge highlights the average time in hours it takes for a developer to merge a pull request, starting from the moment a team member creates a pull request. Comprehending your time to merge KPI informs code leaders about the resources needed to review and implement work.
21. Reliability
The reliability KPI draws upon other metrics to paint a better picture of your code's quality and your team's overall QA process. The ideal reliability metric differs depending on the development team's primary focus. For example, an organization offering cloud services may focus on mean time recovery as one of its reliability metrics.
Why do engineering KPI metrics matter?
It's difficult to make progress when you're in the dark. Engineering KPIs illuminate your development cycle, using meaningful data to improve efficiency and processes. From better resource management to aligning goals, here are five reasons engineering KPIs should matter to your team.
- Improved team performance: KPIs enable your team better to understand current efficiency and performance levels via relevant metrics.
- Team goal alignment: KPIs ensure everyone is on the same page by clearly defining objectives.
- Continuous improvement: KPIs are ongoing, helping teams evaluate and improve themselves continually.
- Finer resource management: KPIs call out how teams utilize resources, highlighting potential inefficiencies for future improvement.
- Better quality assurance: KPIs can highlight problems that arise when pushing new releases, thus highlighting how the team can improve the QA process.
How should you choose engineering KPIs?
To choose the correct engineering KPIs for your organization, begin by addressing the question at the heart of every KPI: What do you wish to improve? Businesses utilize KPIs to help teams improve metrics reflecting their primary goals. For example, if a software development company wants to improve its customer satisfaction rate by reducing buggy software, it might choose to focus on the code quality KPI.
Identifying organizational and team-specific KPIs will help decision-makers choose the ones best suited to their needs. It's also important to select KPIs that rely on metrics you can actually access. For example, you would only choose improve team collaboration if you had a tool like Pluralsight Flow to give insights into operational patterns.
FAQ
You may have additional questions about engineering KPIs and how to utilize them for your development team. Here are the most frequently asked questions about engineering KPIs and simplified answers.
What is an example of a SMART KPI?
A SMART KPI is a key performance indicator developed with the SMART goal acronym in mind.
- Specific: The KPI should be apparent, and the goal should be clear.
- Measurable: The KPI should use a quantifiable metric that can be measured.
- Attainable: The KPI should point toward an achievable goal.
- Relevant: The KPI should be relevant to your team's goals.
- Time-bound: The KPI should define a timeframe (daily, weekly, monthly, etc.) for analyzing associated metrics.
Here’s an engineering KPI example with SMART consideration:
A team wishes to reduce project delays caused by scope changes (specific) resulting from fixable team inefficiencies (attainable). They opt to track the scope change KPI (measurable) to see how it leads to delays over the next three months (time-bound). The organization's overall goal is to increase code deployment speeds, which software leads know decreased scope changes will help (relevant).
What is an engineering team scorecard?
An engineering team scorecard is a document that summarizes critical metrics and KPIs relevant to a team's performance and efficiency. Scorecards allow development team members to quickly understand their progress toward a goal and how they might make adjustments to achieve it.
Enable critical engineering KPIs with the power of Pluralsight Flow
Metrics and engineering KPIs can quickly become overwhelming as your development team moves forward. For metrics to be efficient, they need to be consistently updated and analyzed. Many teams use software solutions like Pluralsight Flow to help them achieve their development goals.
Flow enables engineering insights for your team, from engineering managers and leaders to software developers. Automatically make use of KPIs such as lead time for changes, deployment frequency, and time to restore service and follow actionable indicators to make a difference.
It's time to reduce friction, leverage actionable insights, and improve the health of your developers and engineers—transform your team with Pluralsight Flow.
