15 Proven Software Development KPIs To Steal From Tech Giants

Lead time

First, we’ll look at lead time. Lead time is the total time taken from the moment a new task or feature is requested until it is delivered to production and ready for use. This KPI encompasses all stages of the development process, including initial requirements gathering, coding, testing, and deployment. Essentially, lead time measures the efficiency and responsiveness of the development team in transforming ideas into functional software. It’s an effective high level measure of the efficiency of the entire software development process.

What you need to know:

• Shorter lead times lead to quicker delivery of features and bug fixes.
• Long lead times can be indicative of process inefficiencies that lead to frustration and burnout among team members.
• While overall lead time data can be helpful as a big-picture indicator of efficiency, often other, more nuanced software development metrics are needed to pinpoint specific process inefficiencies.

Example:

Branch creation to merge time

Similar to lead time, branch creation to master merge time can help measure the efficiency of a team. But, instead of an end-to-end measure of time, this software development KPI just looks at a subset of the process: the duration between the creation of a feature or bug-fix branch and its successful merge into the master branch. It zeros in on the development speed itself, removing requirements gathering, testing, and deployment from the picture.

What you need to know:

• This KPI provides insights into potential bottlenecks in the development process.
• Reducing branch creation to master merge time can lead to faster delivery of features and bug fixes, better alignment with agile practices, and enhanced overall productivity.

Example:

Stripe’s teams track branch creation to merge time.

Commit to deploy time

The commit to deploy time software development KPI measures the duration between a developer's code commit and the deployment of that code to the production environment. In essence it’s a measure of the period of time in the workflow after a new branch is merged. Monitoring commit to deploy time helps organizations identify delays in the CI/CD process, such as prolonged build times, extended testing phases, or deployment bottlenecks.

What you need to know:

• This KPI provides insights into the efficiency of the CI/CD pipeline, highlighting areas that may need optimization, such as faster build processes or more efficient testing procedures.
• By minimizing this time, teams can ensure that new features, improvements, and bug fixes reach users more quickly.

Example:

Atlassian’s engineering teams track time from commit to deploy.

Number of pull requests per week

The number of pull requests per week is a measure of the frequency of code changes submitted for review and integration into the main codebase. This KPI is crucial for understanding the pace and activity level of the development team. A higher number of pull requests can indicate a healthy, active development process where team members are regularly contributing code. Conversely, a low number of pull requests might signal potential issues such as roadblocks, lack of progress, or a culture that isn’t agile.

What you need to know:

• Measuring the number of pull requests per week involves tracking the submissions made by developers for code review and integration into the main codebase, reflecting the team's activity level and collaboration.
• Increasing the number of pull requests per week can lead to more frequent code reviews, faster integration of changes, and a more dynamic and agile software development process, promoting continuous improvement and collaboration.

Example:

Uber’s teams track weekly # of pull requests per engineer.

Code reviewer response time

Code reviewer response time is a measure of the duration between the submission of a code review request and the initial response from a code reviewer. Monitoring code reviewer response time helps organizations identify delays in the review process, which can impact the overall development cycle. Quick and effective code reviews are essential for maintaining code quality, detecting bugs early, and ensuring that new features and fixes are integrated smoothly.

What you need to know:

• Reducing code reviewer response time can lead to faster integration of changes, improved code quality, and a more streamlined software development process.
• As an alternative, some teams opt to track the number of code reviews in a given time period, to help with early identification of bottlenecks in the workflows.

Example:

LinkedIn’s engineering teams track code reviewer response time.

Percentage adoption of standards

The percentage adoption of standards measures the extent to which a team or organization adheres to established coding standards, best practices, and guidelines. This KPI is crucial for maintaining consistency, improving code quality, and ensuring that all team members follow the same protocols, which can significantly reduce technical debt and enhance maintainability.

What you need to know:

• Measuring the percentage adoption of standards involves regular code audits and automated tools that check for compliance with coding guidelines, best practices, and documentation requirements.
• High adoption rates indicate a well-aligned team that understands and values the importance of following agreed-upon standards, leading to more reliable and readable code.

Example:

Uber’s engineering teams track % adoption of standards.

Build time

Build time is a KPI that measures the duration required to compile and build the software from source code to an executable state. This KPI is crucial for understanding the role that build time plays in the development and continuous integration (CI) processes. Faster build times are essential for maintaining a productive development workflow, as they enable quicker feedback and more rapid iterations. While on the surface it may seem like a KPI related to a team’s efficiency, this kind of KPI tells us a lot about the quality of the code that’s being pushed out.

What you need to know:

• Monitoring build time helps organizations identify inefficiencies in the build process, such as suboptimal configurations, excessive dependencies, or outdated tooling.
• Reducing build time can lead to more rapid development iterations, quicker feedback loops, and higher developer satisfaction.

Example:

LinkedIn’s engineering teams track developer build time.

CI pipeline failure rate

The CI pipeline failure rate measures the frequency at which builds, tests, or deployments fail within the continuous integration (CI) pipeline. Measuring the CI pipeline failure rate involves tracking the number of failed builds, tests, or deployments within a given period, providing insights into the stability and reliability of the CI process.

What you need to know:

• A high failure rate can indicate underlying issues that need addressing to ensure smooth and efficient development workflows.
• Reducing the CI pipeline failure rate can lead to more stable and predictable development cycles, improved developer productivity, and greater confidence in the quality of the code being integrated and deployed.

Example:

Lattice’s engineering teams track CI pipeline failure rate.

Deployment success rate

Deployment success rate, also sometimes known inversely as change failure rate, is a KPI that measures the percentage of successful deployments to production without causing system failures, bugs, or requiring immediate rollbacks. This KPI helps to understand the reliability and stability of the deployment process and the overall quality of code changes being pushed to production.

What you need to know:

• This KPI highlights areas where the deployment process may be failing, enabling teams to identify and address root causes, such as improving testing practices or enhancing code review procedures.
• Increasing the deployment success rate can lead to more reliable and predictable releases, reduced incidence of bugs and system failures, and a higher level of confidence in the deployment pipeline.

Example:

Amplitude’s engineering teams track change failure rate.

Time to restore services

Time to restore services (TTRS) measures the duration required to restore a system to full operational status after a service disruption or outage. Rapid restoration of services is essential to minimize downtime and mitigate the impact on users and business operations. Monitoring TTRS helps organizations identify weaknesses in their incident response protocols, such as delays in detection, communication issues, or inefficiencies in the recovery process.

What you need to know:

• This KPI provides insights into the efficiency of incident response and recovery processes, highlighting areas that may require improvements such as faster detection, better communication, or more effective recovery strategies.
• Reducing TTRS can lead to minimized downtime, enhanced user satisfaction, and reduced financial and operational impact of service disruptions.

Example:

Peloton’s engineering teams track time to restore services.

Stability of services / apps

The stability of services or applications is a KPI that measures the consistency and reliability of software performance over time. Monitoring the stability of services/apps helps organizations identify recurring issues, such as bugs, performance bottlenecks, or infrastructure problems, that may affect the user experience. By focusing on improving stability, teams can enhance the overall quality and dependability of their software products.

What you need to know:

• Measuring the stability of services/apps involves tracking metrics such as uptime, the frequency of crashes, code coverage, and the occurrence of performance issues.
• This KPI highlights areas where the application or service may be prone to instability, enabling teams to identify and address underlying causes such as code defects, inadequate testing, or infrastructure limitations.

Example:

DoorDash’s engineering teams track stability of services/apps.

Developer satisfaction

Developer satisfaction or sentiment is becoming increasingly popular as organizations recognize this KPI’s profound impact on team performance and company success. Satisfied developers tend to be more engaged and productive, which enhances overall team performance. High satisfaction levels also reduce turnover, saving costs related to hiring and training new employees.

What you need to know:

• By emphasizing developer satisfaction, organizations can create an environment where team members feel valued, supported, and motivated to perform at their best.
• Use one-on-one meetings to gather information, or utilize tools like pulse surveys and feedback platforms to collect real-time data.
• Correlate satisfaction levels with productivity metrics to identify trends and areas for improvement.

Example:

Etsy’s engineering teams track developer sentiment.

Perceived productivity

Perceived productivity, also known as self-reported productivity, is an increasingly recognized KPI in modern software development teams. It involves asking developers to assess their own productivity levels, typically through regular surveys or feedback tools. This KPI provides valuable insights into how team members feel about their work output and efficiency, offering a subjective complement to more traditional, objective productivity metrics.

What you need to know:

• Measuring perceived productivity involves conducting regular anonymous surveys and using digital feedback tools for continuous, real-time reporting.
• This KPI can reveal hidden obstacles or inefficiencies that might not be apparent through quantitative data alone. For instance, if many team members report low perceived productivity, it might indicate issues such as unclear requirements, insufficient tools, or disruptions in workflow.

Example:

Intercom tracks developers’ perceived productivity.

Focus time per engineer

Focus time per engineer measures the amount of uninterrupted, productive time developers are able to work on their tasks. This KPI is crucial in understanding how effectively engineers can engage in deep work, which is essential for tackling complex problems and producing high-quality code.

What you need to know:

• Measuring focus time involves tracking the amount of uninterrupted work time developers have each day, often using tools that monitor activity and distractions.
• This KPI may highlight potential issues with interruptions, meetings, or other distractions.
• Improving focus time can lead to higher productivity and code quality, as well as increased job satisfaction and reduced burnout among developers.

Example:

Intercom tracks weekly average focus time per engineer.

Ease of delivery

Self-reported ease of delivery is generally a measure of how smoothly and efficiently developers perceive the process of deploying software updates, features, or bug fixes to production. Monitoring self-reported ease of delivery helps organizations identify obstacles that may hinder smooth deployments, such as manual processes, integration issues, or lack of automation, from the developers' perspective.

What you need to know:

• Gathering this feedback directly from developers provides valuable insights into the practical challenges they face and areas for improvement.
• Improving self-reported ease of delivery can lead to more consistent and predictable release cycle time, lower deployment-related stress, and an embrace of a more modern agile methodology. and responsive development team.

Example:

Google’s engineering teams track ease of delivery.