In software engineering, tracking progress isn't just beneficial; it's essential. While assessing individual developer productivity can be challenging, team performance is more accessible. Engineering KPIs can clearly show where your team stands and the road ahead, helping you make informed decisions regarding your products, features, and processes. Moreover, these metrics can evaluate the efficiency and effectiveness of your dev teams, ensuring that you are on track to achieve your engineering goals.

But the question remains — which metrics should you focus on? Identifying your KPIs for engineering teams is crucial to steer your organization towards success. In this guide, we’ll cover the top engineering KPIs central to project success, fostering growth, and achieving long-term business objectives. By adopting a targeted approach to gauging performance through KPIs, your team can work more smoothly, meet deadlines effectively, and continuously enhance the quality of your software products.

What are engineering KPIs?

Engineering key performance indicators (KPIs) are measurable values that help software engineering teams determine their efficiency in achieving project and business objectives. KPIs for engineering teams offer a quantitative analysis of your team’s performance, enabling project managers to identify and address specific issues and schedule tasks for maximum developer productivity.

Benefits of measuring engineering KPIs

• Informed decision-making: KPIs enable data-driven decisions, helping you base strategies on facts and figures rather than assumptions. Clear visibility into a specific set of metrics helps quantify work progress and keep the team focused.
• Improved efficiency: By regularly measuring KPIs, teams can identify bottlenecks and streamline processes, leading to higher efficiency in project execution. These blockers can be anything from burnout to increased cycle time; teams must identify, analyze, and fix these issues to improve efficiency.
• Quality assurance: KPIs allow teams to maintain a stringent check on the software's quality, ensuring that the end product meets the necessary standards and expectations. Better product quality, in turn, leads to better customer experience and retention rates.
• Cost management: With clear insight into the performance metrics, managers can better control project costs, avoid overruns, and optimize resources effectively.
• Team morale and satisfaction: Meeting KPIs consistently fosters a sense of achievement and satisfaction among team members, enhancing morale and encouraging a healthy working environment.

Software engineering KPIs guide teams toward their goals, focusing on continuous improvement and operational efficiency. By identifying and focusing on the right KPIs, software engineering teams can work towards achieving both short-term objectives and long-term organizational goals.

Types of engineering KPIs

There are various categories of engineering KPIs, each serving a unique purpose and offering different insights into the performance and processes of your engineering team. Let us dive into these categories and understand what they entail:

Quantitative indicators

These KPIs for engineering teams can be precisely measured and presented with numerical values. Examples include lead time for changes, cycle time, and deployment frequency. Qualitative indicators are ideal for tracking performance over time and setting clear, measurable goals for the team, thus aiding in process optimization and productivity enhancement.

Qualitative indicators

Qualitative indicators are engineering KPIs based on qualitative data, involving subjective assessments and evaluation through feedback rather than exact measurements. Examples include code review feedback, stakeholder satisfaction, employee net promoter score (NPS), and developer experience indicators.

Qualitative indicators are essential in understanding the softer aspects of the engineering process, which, while not precisely measurable, can impact the overall working environment and product quality.

Leading indicators

These software engineering KPIs offer insights into future outcomes and can help predict a project’s success or failure based on current data. Lead time for changes and PR size are examples of leading indicators. Leading indicators are crucial for proactive management and forward planning, allowing teams to anticipate challenges and opportunities based on current performance metrics.

Lagging indicators

Lagging indicators reflect historical data, showcasing the results achieved over a certain period. Metrics like change failure rate (CFR), mean time to recover (MTTR), and deployment frequency fall under this category. These engineering KPIs assess the team's performance retrospectively, helping understand the successes and areas for improvement to guide future management decisions.

Input indicators

These are KPIs that measure the resources utilized during software engineering processes. You can consider resource allocation and investment profiles as input indicators. Input indicators ensure optimal utilization of resources, helping manage costs and allocate resources in projects with many moving pieces.

Top 13 KPIs for engineering teams

It’s necessary to understand the significance of each KPI before we can interpret them in the context of developer productivity.

1. Lead time for changes

Lead time for changes refers to the time between the inception of a change — such as the identification of a new feature, bug fix, or any other modification — to the point where this change is successfully implemented and goes live. It captures the entire process from identification and development to testing and deployment.

For example, imagine a scenario where a financial application has identified a critical security vulnerability. In such a scenario, a reduced lead time for changes would be vital to swiftly develop and deploy a fix, safeguarding user data and maintaining the trust of the clientele.

Measuring lead time for changes offers a clear perspective on the agility and responsiveness of the software engineering team. It helps teams understand how quickly and efficiently they can move changes from the drawing board to production.

2. Deployment frequency

Deployment frequency refers to the number of times a team deploys code into the production environment over a defined period. This engineering KPI reflects the team's agility and ability to deliver updates, new features, or fixes at a consistent pace. Generally, higher deployment frequency indicates a healthy, agile, and responsive development environment. Google’s Accelerate State of DevOps Report classifies on-demand deployment with multiple deploys per day as a high deployment frequency.

If a streaming service wants to constantly roll out updates to enhance user experience, a high deployment frequency would allow them to swiftly respond to user feedback and implement improvements, therefore maintaining a dynamic and user-friendly environment.

Measuring deployment frequency diligently allows software engineering teams to have their fingers on the pulse of the project’s progress. It encourages a culture of continuous improvement, agility, and responsiveness within the development lifecycle.

3. Change failure rate (CFR)

Change failure rate (CFR) measures the rate of failure of deployments. In simple terms, it calculates how often something goes wrong when changes are introduced to a production environment. A lower CFR indicates a healthy development pipeline, portraying a team’s competency in implementing changes successfully without hiccups. It helps understand the risks associated with the deployment process and fosters a culture of accountability and precision.

By focusing on a low change failure rate (CFR), software engineering teams can hedge against potential failures, ensuring a smoother, more reliable deployment process. Low CFR testifies to the team’s ability to predict risks and navigate the development landscape with a precision-driven approach.

4. Mean time to recover (MTTR)

Mean time to recover (MTTR) is a critical KPI in software engineering that refers to the average time taken to restore a system or application after a failure or downtime. It captures the period from detecting an issue to its eventual resolution, where the team returns the system to its operational state. The goal is to keep this timeframe as low as possible, ensuring minimal disruption and swift recovery from unforeseen hiccups.

For example, Imagine what happens if a cloud storage service faces an outage that prevents users from accessing their stored files. A reduced MTTR would mean the team can swiftly rectify the issue, thereby mitigating any inconvenience caused to the users.

Measuring MTTR is akin to having a health check mechanism in place. It assesses the engineering team's efficiency in troubleshooting and rectifying issues and also plays a vital role in minimizing downtime, thereby upholding a positive user experience.

5. Cycle time

Cycle time refers to the duration from the inception of a task or a project to its eventual completion. It is the time taken to transform an idea or a requirement into a tangible output, such as a software update or a feature release. By minimizing cycle time, teams can achieve faster deliveries and a more agile development process.

Understanding and optimizing cycle time is essential to scrutinize the effectiveness of the development process. It offers insights into how swiftly a team can respond to requirements and deliver solutions. Measuring cycle time as an engineering KPI fosters a culture of efficiency and agility and enables a team to deliver quality outputs on time.

6. Pull request (PR) size

A Pull request (PR) is a way of submitting contributions to a software project. The "PR Size" refers to the amount of change contained within a pull request, usually measured by the number of lines of code altered or the number of commits included in the request. Measuring the PR size helps maintain a balanced workflow by avoiding overly complex or substantial changes that can slow the review process and potentially introduce bugs.

If a team is working on a government service portal, opting for smaller PR sizes will allow them to facilitate more frequent deployments. This allows for a faster response to user feedback and a more agile development process, enhancing user satisfaction and trust in the portal.

Measuring pull request (PR) size as a KPI in the software engineering pipeline promotes meticulous oversight and agile responsiveness. It is essential in maintaining a healthy, agile, and efficient software engineering pipeline. It facilitates smoother reviews, quicker integration, and a more manageable and understandable codebase.

7. Merge frequency

Merge frequency refers to the regularity with which code changes are integrated, or 'merged,' into the main codebase. This KPI reflects the pace at which a development team works and is often a direct indicator of the team's agility and efficiency. High merge frequency denotes a continuous and systematic integration of codes, which is a hallmark of DevOps best practices.

Maintaining a high merge frequency fosters agility and reduces the risk of integrating large chunks of code changes, which might introduce bugs or conflicts. It enables teams to identify and address issues early in the development lifecycle.

8. Investment distribution

Investment distribution is about understanding where your engineering resources are spent. It breaks down how much effort goes into new feature development compared to tasks like maintenance or KTLO (Keeping the Lights On).

Some of the benefits of measuring investment distribution include:

• Align effort with objectives: By analyzing investment distribution, teams can ensure their efforts match business goals. For instance, if a business aims for innovation, more resources should go into new feature development rather than just maintenance.
• Build trust with business teams: This KPI allows engineering teams to showcase where their time is being spent transparently. Doing so creates more transparent communication, helping business teams understand the constraints and capacities of the engineering team.
• Customized insights: While it's helpful to see how resources are divided between tasks like new features and maintenance, the flexibility of investment distribution allows teams to measure time spent on specific product themes or even crucial customer accounts. This granular insight helps in making strategic decisions.

For example, imagine a scenario where an e-commerce platform wants to invest in new feature development, introducing AI recommendations, AR product previews, or one-click checkouts that can potentially increase sales. However, if the core infrastructure, like payment gateways or user account systems (the KTLO aspects), isn't given enough attention, it might lead to downtimes, payment failures, or security breaches. A major failure in these fundamental areas can damage the company's reputation and customer trust, possibly leading to more significant financial losses than not having the new features. That’s why clearly understanding investment distribution between new features and KTLO is paramount.

Investment distribution is more than just a metric; it's a lens that provides a clearer view of software engineering's impact and alignment with overall business goals. Through its insights, companies can drive efficiency, build trust, and ensure their engineering efforts are channeled in the most impactful direction.

9. Code churn

Code churn refers to the volume of code that is repeatedly altered, including code that has been added, modified, or deleted over a specific period. As a KPI, it offers a snapshot of the stability and maturity of a project, with high levels of churn potentially indicating instability, ongoing refinement, or enhancement processes.

For example, picture a scenario where a fintech firm is developing a new app. A sudden spike in code churn could indicate that the development team is struggling to integrate a newly requested feature. Recognizing this early on allows for timely support and adjustments, averting a potential delay in the launch.

Measuring code churn helps identify potential issues early in the development cycle, facilitating timely interventions. While a certain degree of churn is expected and healthy as it signifies improvements and refinements, an excessively high churn rate can be a red flag, pointing towards indecisiveness or a lack of clear direction, potentially delaying the project and increasing costs.

10. Uptime

Uptime refers to the time a system, application, or service remains operational and accessible without experiencing downtimes or outages. As a KPI, it is a critical gauge of reliability and service quality, often expressed as a percentage, where a higher percentage indicates better performance and stability.

Measuring uptime as a KPI in the software engineering pipeline prioritizes uninterrupted, reliable service, creating a trust bedrock with the users. For example, an online streaming service would want to ensure a 99.99% uptime to provide users with access to their favorite content anytime, without interruptions, for a seamless customer experience.

High uptime directly translates to reliable and consistent service for users. It’s a measure of a well-engineered product equipped to handle real-world demands without buckling under pressure. An excellent uptime KPI reflects a team's foresight in anticipating issues and implementing robust solutions, showcasing the product’s reliability.

11. Sprint burndown & release burndown

Sprint Burndown is a graphical representation that showcases the amount of work remaining in a sprint, often daily. The graph compares the actual progress against the expected progress, serving as a tool to foresee whether the team is on track to complete the planned tasks within the designated sprint timeframe.

Release Burndown, on the other hand, focuses on a larger timeframe, covering the entire release period. It charts the remaining work to be completed before a product release, offering a broader perspective on the project's progress over consecutive sprints.

For instance, imagine if an engineering team is in the development phase of a new mobile application. The sprint burndown chart shows a consistent reduction in outstanding tasks, painting a picture of a team collaborating and on pace to meet the sprint goals. This visual tool empowers the team to adjust workload dynamically, ensuring a balanced pace and averting burnout.

12. Developer well-being

As an engineering KPI, developer well-being measures the holistic health and satisfaction of software engineers. It's not just about the absence of negative factors like stress or burnout but also the presence of positive ones like motivation, engagement, and a sense of accomplishment.

Measuring developer well-being can provide a deeper understanding of:

• Impact on productivity: A developer's mental and emotional health directly correlates with productivity. Their performance can dip when developers feel overwhelmed, unrecognized, or unsupported. On the other hand, a developer who feels motivated and supported is more likely to be proactive, efficient, and innovative.
• Retention and recruitment: Employee well-being plays a significant role in retention. Developers who feel their well-being is a priority are more likely to stay with an organization. Moreover, in an industry as competitive as software development, companies prioritizing developer well-being have a distinct advantage in attracting top talent.
• Encouraging collaboration and team cohesion: Well-being is not just an individual concern. Developers in good spirits are more likely to collaborate, share knowledge, and help teammates, fostering a positive team environment.

Prioritizing and measuring developer well-being isn't just an altruistic goal; it's a strategic one. In software engineering, projects can be complex and demanding, and ensuring that developers are mentally and emotionally equipped to handle challenges can be the difference between success and failure.

Next steps

Tracking engineering KPIs is essential to understand the overall performance of your development team and develop a deeper understanding of work patterns. Clear, actionable insights can lead to a happier and more productive engineering team, laying down a path for sustained success in your software projects. Remember, you can only manage what you can understand.

If you're aiming to get a clear picture of how your engineering team is doing, DevDynamics is here to help. Think of it as your team's personal analytics assistant, helping you measure essential KPIs for engineering teams. We offer auto-generated reports that provide insights into your developers’ work patterns, help catch signs of burnout early, and point out areas where things can improve. Simply put, DevDynamics offers you the analytical capabilities to track the engineering KPIs you need to keep your team happy, productive, and working together smoothly.