DORA metrics
Best Practice
Engineering Intelligence

15 Engineering KPIs to Improve Software Development

Tracking the right KPIs in software development is tricky—leading indicators, lagging indicators, input-based, output-based…there’s lots to consider beyond the marker itself. Here’s a few to get the conversation started.

Lauren Craigie
March 7, 2024

Software engineering key performance indicators (KPIs) help engineer leadings keep teams accountable while ensuring focus on highest leverage activities. They are essential for driving process improvement, managing risks, supporting data-driven decision making, and ensuring customer satisfaction. Without KPIs, teams may encounter challenges related to visibility, efficiency, decision-making, quality, and customer satisfaction, which can ultimately impact project success and organizational performance. This article will explain what software engineering KPIs are, why they are important, how to choose the right ones, and how to track and improve them over time. 

What are software engineering KPIs?

KPIs or key performance indicators refer to quantifiable metrics used by organizations to track progress made towards critical goals and business objectives. KPIs evaluate the degree of success of a specific activity, initiative, project, process, or product. 

While KPIs are like milestones guiding us towards specific goals, metrics are the raw data we collect along the way. KPIs are directly tied to big-picture objectives, giving you a clear measure of how you’re doing in reaching those goals. Metrics, on the other hand, cover a wider range of data points to analyze performance. They're helpful for understanding different aspects of work, but they might not always line up perfectly with the main objectives. In some cases, multiple metrics can be used to compose a KPI.

Software engineering KPIs are specifically used to evaluate the performance, workflow efficiency, and effectiveness at various stages of building, testing, and releasing software. These metrics assess and measure key aspects across the software development lifecycle, providing insights into the health, productivity, quality, and business impact of technology initiatives. They play a critical role in helping teams assess their performance, identify areas for improvement, and make data-driven decisions to optimize their software development processes.

The selection of KPIs is contextual and depends on factors such as the organization's industry, size, business model, and strategic focus. For example, a software development company aiming to improve time-to-market may prioritize KPIs related to lead time, cycle time, and deployment frequency. On the other hand, a company emphasizing product quality and customer satisfaction may prioritize KPIs such as defect density, customer satisfaction scores (NPS), and code coverage. Companies like Uber, Netflix, and Facebook have company specific KPIs like rider acceptance, watch time, and social engagement, which are comparies of multiple engineering KPIs to ensure quality products. We’ll discuss KPI selection in greater detail later.

15 software engineering KPIs to start tracking

Different organizations may prioritize different KPIs based on their unique objectives. By aligning KPIs with specific organizational goals, businesses can ensure that they are measuring the aspects of performance that are most critical to their success.

DORA, or DevOps Research and Assessment, is a research organization that focuses on understanding and improving software development and delivery practices. They recommend five KPIs to evaluate the performance of DevOps teams, some of which we cover here. You can learn more about their metrics here.

While the specific measures that prove most valuable depend largely on the organization, below are 15 commonly used software development KPIs that provide value to many teams:

  • Velocity: Velocity measures a team’s rate of completing user story points during sprints. Tracking this helps teams have more predictable roadmap planning. Higher, consistent velocity signals greater throughput. Teams can increase velocity with strategies like breaking down epics, alternating issue types between sprints, implementing daily standups, and ensuring alignment between engineering and product. Be mindful not to use velocity to judge performance between teams or individual engineers; velocity is dependent on a number of factors, including tooling, scope changes, and technical debt.  
  • Lead time: Lead time represents the total duration from the initiation of a task to its completion, spanning the full development cycle. This time includes both active work time and any waiting time spent in queues or backlogs until the solution is deployed. This metric serves as a barometer of workflow efficiency, allowing teams to identify inefficiencies and streamline their development pipelines for accelerated delivery cycles. Shorter lead times translate to faster delivery of coding changes, allowing teams to rapidly iterate based on user feedback. Strict work-in-progress (WIP) limits, smaller tasks sizes, and skill-based assignments optimize lead time.
  • Cycle time: Cycle time specifically measures time from when work begins on an item until it’s fully deployed. Unlike lead time, cycle time only begins at coding time and doesn’t include time spent in a queue. By minimizing unnecessary coding efforts via test automation and infrastructure efficiency, engineering teams can reduce cycle time dramatically and can realize massive gains in release velocity. Most organizations have cycle times of 1–2 weeks, while high performing teams can have cycle times of less than a day.
  • Change failure rate: Change failure rate is the percentage of deployments causing production incidents that directly impact end-user experience and team productivity. The most elite teams have a change failure rate of 5% or less, and high performing teams have a rate around 10%, according to DORA. Robust QA processes, gradual rollouts, orchestration tools, and redundant safeguards are prerequisites for low production failure rates. 
  • Defect density:  Defect density quantifies the number of errors or bugs per unit of codebase, usually measured in defects per thousand lines of code (KLOC). This metric offers insights into the quality and robustness of software systems. By tracking defect density, teams can proactively address underlying issues, improve code quality, and fortify their applications against vulnerabilities and failures. Defect density helps engineering teams assess and prevent issues by tracking bugs at the codebase level. 
  • Test coverage: Test coverage or code coverage measures the percentage of all code paths and branches that are executed during automated testing, such as unit, integration, and end-to-end testing. Low coverage leaves the risk of undetected defects reaching users and places a higher burden on the engineer to perform manual testing. Generally acceptable coverage hovers around 70–80%, although some mature teams maintain rates exceeding 90%. Invest in testing frameworks that make writing tests easy and create a culture that values testing and code quality to increase test coverage. 
  • Deployment frequency: According to DORA, deployment frequency is “how often an organization successfully releases to production.” The pace that teams push code changes into production environments greatly impacts cycle time and feedback loops. Elite teams generally deploy on-demand, and high-performing teams deploy daily, so they can respond better to user needs and drive greater improvement through faster experimentation. Make sure not to sacrifice quality and testing to increase deployment frequency. Learn more about deployment frequency here.
  • Mean time to recovery (MTTR): MTTR represents the average duration between detecting an incident and restoring normal operation. DORA now calls this “failed deployment recovery time.” This metric provides a key signal regarding reliability and operational rigor. No matter how robust the QA and release procedures, some percentage of changes will degrade service levels or cause full service downtime. Elite teams generally maintain a MTTR under 1 hour through well-documented runbooks, redundancy, expert staffing and extensive monitoring, while high-performing teams recover in less than one day.
  • Technical debt: Technical debt is a concept in software development that refers to the accumulated cost of additional rework, maintenance, or refactoring required due to choosing a fast but suboptimal solution instead of a more robust one that would take longer to implement initially. Technical debt tends to slow development and lower morale among engineers. There is no single benchmark for this metric; rather, the focus should be on minimizing debt before exceeding ROI tradeoffs. Common tactics include allocating sprints to refactors and code improvements, establishing architectural principles, and empowering teams to make decisions around technology and code health. 
  • Sprint burndown: Sprint burndown is the amount of work completed versus the time remaining within a sprint in Agile software development, generally measured in number of story points. A sprint burndown chart serves as a tool for tracking the progress of the development team towards completing the committed work for the sprint. The rate at which teams complete sprint items provides real-time information regarding scope and throughput. There are no universal benchmarks, although scrums with consistent velocities present consistent burndown curves across sprints.
  • Release burndown: Whereas sprint burndowns gauge output and speed, release burndowns provide visibility into progress towards broader milestones and feature launch targets. Understanding the release progress provides team members with insights into the team's performance, facilitating early risk identification and enabling data-driven decision-making throughout the release cycle. Teams also use release burndowns to identify unanticipated velocity slowdowns, so timeline commitments can adapt accordingly.
  • Cumulative flow: Cumulative flow is often synonymous with a cumulative flow diagram (CFD), a powerful tool for visualizing and analyzing the flow of work in Agile environments. It typically plots the number of work items in various states, such as "To Do," "In Progress," and "Done" against time intervals, such as days or weeks. As time progresses, the diagram shows how work items move through each stage. By providing insights into workflow efficiency, bottlenecks, and opportunities for improvement, CFDs empower teams to make data-driven decisions and continuously evolve their processes to deliver greater value to customers. For example, accumulations in a “Testing” state could indicate automation gaps, while build-up in “In Progress” implies an overloaded development team. Leaders should prioritize efficiency by breaking down work into manageable pieces and ensuring that these pieces move smoothly through established, reliable processes.
  • Flow efficiency: Similar to cumulative flow, flow efficiency measures the proportion of time work is actually being advanced versus waiting on queues, rework, or other delays. Simply put, it’s the ratio between the value-adding time and the lead time. Achieving maximum flow efficiency relies on practices such as breaking down work into smaller, manageable units, setting strict limits on work in progress to prevent bottlenecks and overload, ensuring teams have the right mix of skills and resources, and identifying and removing obstacles hindering workflow. Optimizing flow efficiency allows organizations to streamline processes, reduce waste, and accelerate value delivery, ultimately raising productivity and customer satisfaction.
  • Bug rate: The bug rate is used to quantify the number of bugs or defects discovered during a specific phase, such as development or testing phases. Bug rate provides insights into the quality of the software by indicating the frequency of defects discovered, as opposed to defect density, which tracks the existing defects in the source code. Monitoring the volume of bugs introduced with each release provides a critical baseline for assessing improvements (or degradations) in code quality and development rigor over time. To reduce high bug rates, emphasize test automation, thorough code reviews and debugging, and tightly scoped issues, while limiting technical debt. 
  • Customer satisfaction (net promoter score): Customer satisfaction, specifically measured through the net promoter score (NPS), evaluates how content and happy users are with the software products delivered by engineering teams. NPS provides valuable insight into whether development initiatives genuinely create value for users rather than simply producing output. While inherently subjective, customer satisfaction, including NPS, can be quantified using specific metrics and standardized surveys and rating systems. 

Why are software engineering KPIs so important?

Software engineering KPIs offer various benefits that are instrumental in driving organizational success and achieving strategic objectives.

  • Performance measurement: Software engineering KPIs enable organizations to quantitatively measure the performance of their development teams and processes. By tracking metrics such as code churn, lead time, and deployment frequency, teams can gain insights into their efficiency and productivity levels, identifying areas for optimization and improvement.
  • Quality assurance: Creating high-quality products and systems is essential in software development, and KPIs provide valuable insights into the quality of deliverables. Metrics such as defect density, code/test coverage, and mean time to recovery (MTTR) help teams assess the robustness and reliability of their software, guiding efforts to enhance product quality and minimize defects.
  • Process improvement: Software engineering KPIs serve as benchmarks for evaluating the effectiveness of development methodologies and practices. Teams can identify inefficiencies, bottlenecks, and areas ripe for process optimization, fostering continuous improvement and innovation. Useful metrics include those related to backlog health, sprint burndown, and cumulative flow.
  • Risk management: Effective risk management is essential for mitigating project delays, high costs, and potential failures. Software engineering KPIs, such as change failure rate and technical debt, provide early warning indicators of potential risks and vulnerabilities, enabling proactive mitigation strategies and risk mitigation efforts.
  • Resource allocation: Optimal resource allocation is critical for maximizing productivity and minimizing waste in software development projects. KPIs related to resource utilization and efficiency help organizations allocate human and technical resources effectively, ensuring that teams have the necessary support and capacity to deliver projects on time and within budget.
  • More effective project planning and execution: Software engineering KPIs provide valuable data for project planning, estimation, and execution. By leveraging metrics such as velocity, backlog health, and cycle time, teams can make informed decisions about project scope, timelines, and resource allocation, leading to more accurate planning and smoother project execution.
  • Data-driven decision-making: To be data-driven is to base decisions on empirical evidence and quantitative analysis rather than intuition, personal opinion, or anecdotal evidence. Software engineering KPIs empower organizations with actionable insights and data-driven intelligence, enabling stakeholders to make informed decisions based on empirical evidence.
  • Benchmarking: Benchmarking against industry standards and best practices is essential for assessing performance and identifying areas for improvement. Software engineering KPIs provide valuable benchmarks for comparing organizational performance against industry norms, enabling organizations to set realistic goals for improvement. KPIs also facilitate benchmarking internally against historical performance to monitor growth over time.
  • Agile and iterative development: Software engineering KPIs are particularly beneficial in Agile environments. Agile methodologies emphasize continuous improvement and adaptation, and KPIs such as sprint burndown and velocity are integral to Agile practices, providing teams with feedback loops and performance metrics to drive continuous delivery. Learn more about specific Agile metrics here.
  • Customer satisfaction: Ultimately, the success of software projects hinges on customer satisfaction. Software engineering KPIs help organizations gauge customer satisfaction levels by tracking metrics such as customer satisfaction scores and user feedback. By prioritizing customer-centric KPIs, organizations can ensure that their software meets user expectations and delivers tangible value.
  • Strategic alignment with business objectives: Aligning software engineering KPIs with broader business objectives is crucial for driving organizational success. By selecting KPIs that directly support strategic goals and initiatives, organizations can ensure that their software development efforts are aligned with the overall mission and vision of the business, driving value creation and competitive advantage.

How to choose the right software engineering KPIs

Choosing the right KPIs is essential to making metrics productive and leading successful projects, while choosing the wrong ones can cause teams to waste cycles optimizing the wrong things. Consider the following when selecting metrics for your team:

  • Understand organizational goals: Begin by understanding the overarching goals and objectives of your organization. Your KPIs should directly support these strategic initiatives and contribute to the overall success of the business. Identify key areas where software engineering efforts can make the most significant impact in achieving organizational goals, such as improving product quality, increasing customer satisfaction, or enhancing time-to-market. 
  • Consider development methodology: Depending on which Agile methodology your team uses, different metrics will be more appropriate than others. For example, velocity is relevant in Scrum because it measures story point completion over a sprint. If you use Lean, choose metrics around rework rates and cycle time rather than throughput. Learn more about Agile metrics.
  • Identify priority areas: Although factors like quality, security, velocity, and reliability are all important in a software development environment, consider which needs the most improvement or monitoring so that engineering efforts are focused. Priorities can change over time, at which point you can consider tracking different metrics. 
  • Consult stakeholders: Gather input from engineers, testers, architects, product managers, and executives to determine what KPIs they find most beneficial for planning and decision-making. Align on definitions and instrumentation approaches to foster buy-in and commitment to the chosen metrics, increasing the likelihood of successful implementation and utilization.
  • Review industry standards: Frameworks like DORA provide proven, recommended benchmarks for assessing team performance in areas like deployment frequency, lead time, and change failure rate. By taking advantage of established benchmarks, organizations gain valuable insights into industry norms and best practices, enabling them to set realistic goals, benchmark their performance against industry peers, and identify areas for improvement. Additionally, adopting standard metrics enhances credibility and facilitates meaningful comparisons, allowing organizations to measure their progress accurately and make informed decisions to drive continuous improvement.
  • Employ SMART criteria: KPIs should be SMART: specific, measurable, achievable, relevant, and timebound. Vague, ambiguous or unrealistic metrics fail to provide value because they’re difficult to follow and achieve. Read more about setting SMART KPIs here.
  • Mix qualitative & quantitative: Combine quantitative metrics, such as lead time or defect density, with qualitative indicators, such as customer feedback or team morale, to gain a more comprehensive understanding of performance and impact. Qualitative metrics add depth and context to quantitative data, providing insights into user experience, stakeholder satisfaction, and organizational culture that may not be captured by numerical KPIs alone.
  • Consider the development lifecycle: Take a holistic view of the software development lifecycle when selecting KPIs. Incorporate metrics that span the entire lifecycle, from initial planning and requirements gathering to maintenance and support. By considering the entire development lifecycle, you ensure that your KPIs reflect the full spectrum of activities and processes involved in delivering successful software products or services.

Best practices for implementing and using software engineering KPIs effectively

Simply tracking KPIs won’t translate to improvements. Implementing and using software engineering KPIs effectively requires careful planning, communication, and a commitment to continuous improvement. 

Here are some best practices that help teams get the most value from software engineering metrics:

  • Define clear objectives: Establish precise, measurable goals for each KPI so teams understand what they’re aiming for and leaders can properly assess progress. If definitions are vague, it can lead to misalignment and missed opportunities.
  • Implement a mix of leading and lagging indicators: Incorporate both leading indicators (predictive measures) and lagging indicators (historical measures) to provide a comprehensive view of performance and anticipate future trends. Leading indicators enable proactive identification of potential issues and opportunities, while lagging indicators provide valuable insights into past performance and trends.
  • Establish baselines and targets: Establish baseline values for each KPI to serve as reference points for measuring progress and improvement over time. Set achievable and realistic targets and goals for each KPI based on historical data, industry benchmarks, and organizational objectives, to provide clear direction and motivation for improvement efforts.
  • Provide regular feedback: Implement mechanisms for regular monitoring and reporting of KPI performance to provide timely feedback to stakeholders. Use KPI data to hold individuals and teams accountable for their performance and outcomes, promoting a culture of transparency and accountability.
  • Foster a collaborative culture: Position engineering metrics as something done with teams rather than to them, promoting psychological safety and engagement. Encourage the sharing of insights, lessons learned, and best practices related to KPI performance to facilitate organizational learning and improvement.
  • Select KPI tracking technology wisely: Spreadsheets and manual processes don’t scale. Choose engineering intelligence platforms like Cortex that connect existing tools, contextualize trends, and enable progress tracking.

Track and improve software engineering KPIs with Cortex

Engineering leaders know that simply tracking metrics isn't enough to reach objectives — teams need the tools to translate insights into outcomes. Cortex is an internal developer portal that integrates with your engineering tools, applications, and services to provide contextualized data and outcomes. 

Unlike individual metrics dashboards that just inform, Cortex's Engineering Intelligence aggregates signals from systems like GitHub, Jira, and PagerDuty to contextualize metrics and their alignment to an organization's priorities. This lets you track changes in key metrics by incorporating data from multiple sources to identify trends and bottlenecks. Cortex’s Scorecards help organizations efficiently evaluate the health, progress, bottlenecks, and areas of risk using reports that can be customized by team, product, or individual for a detailed analysis.

To learn more, watch our on-demand webinar about engineering productivity or book a demo today.

DORA metrics
Best Practice
Engineering Intelligence
Lauren Craigie
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