We’ve released Leading Indicators—actionable data to help you identify and unblock your top constraint.

Software.com is divided into KPIs and Leading Indicators. Whereas KPIs are lagging metrics that focus on results and outcomes, leading indicators focus on inputs and actions, predicting future performance and trends.

Leading Indicators are designed to be actionable, helping you quickly pinpoint the biggest constraint in your development process. We highlight one high-impact area at a time, making it easy for you to take action without being buried in dashboards or noise.

Constraints vary by team and shift over time. Schedule time with us to review and identify your top constraint together, or feel free to reach out to us at support@software.com.

Our new KPIs report automates the most important metrics for development teams, such as New Deliveries, Rework (vs. New), Deployments, and Lead Time.

The KPIs report features several notable improvements vs. our previous reports:

• Improved terminology: we’ve updated the name of the “Features” metric to “New Deliveries” to avoid confusion with project management terminology. This change is purely naming-related and does not impact how the metric is calculated or displayed.
• Optimal values: based on data for over 700K developers and 10K companies, we’ve added optimal values for New Deliveries per Developer, Rework, and Lead Time.
• Deployments: if you’ve configured deployment methods for your repositories, you will see Deployments per Developer on your KPI report.
• Improved filtering controls: you can filter down to any group or any time period. You can also adjust the granularity to weekly, monthly, or quarterly or view as a table instead of graphs for easier reporting.

If you have any questions, feel free to reach out to us at support@software.com.

We’ve updated the name of the “Features” metric to “New Deliveries” to avoid confusion with project management terminology. This change is purely naming-related and does not impact how the metric is calculated or displayed.

We’re making an adjustment to improve the accuracy of our data — specifically, how we track the amount of code that is New, Churn, and Refactor. These updates provide a more accurate view of changes over time, especially for teams that ship new code iteratively.

You may see adjustments to your metrics and benchmarks—most notably, an increase in New Units per Developer (formally Features per Developer) and a higher percentage of pull requests categorized as New. Since these improvements apply to historical data as well, your data will be uniformly adjusted upward, maintaining all relative differences and trends.

By tracking a more detailed history of file line changes—including both their original position and where they appear after edits—we are now more precisely differentiating between newly written code and modifications to existing work. This added granularity gives a more refined view of how code evolves over time, ensuring that new work is accurately captured and modifications are correctly attributed.

You can learn more about how pull requests are categorized in our documentation. If you have any questions, feel free to reach out to us at support@software.com.

We’ve updated our algorithms to more precisely track your lead time and productivity metrics.

1. Lead Time Stages

   Lead Time is incrementally updated when a PR completes a stage, rather than waiting for the PR to complete all stages. For instance:

   • Previously, Time to Review was updated after a PR was merged.
   • Now, Time to Review is updated when a PR is reviewed.
   • This change ensures that your Lead Time metrics are more timely and reflect an ongoing historical trend.

2. Excluded Pull Requests

   Our algorithm automatically excludes pull requests that are not representative of development productivity, such as those authored by bots and backmerges from main. We also exclude pull requests that are part of your release process (e.g. promoting changes from a release branch to main). We’ve refined our algorithm to more precisely detect these pull requests, such as by detecting re-used branches. Depending on your development process, you may notice a slight increase in your pull request metrics.

If you have any questions about how our algorithms work, feel free to reach out to us at support@software.com.

We’ve released Time-Based Comparisons, which let you compare productivity before/after a key event. Examples of major events that you can measure with this feature include acquisitions, layoffs, leadership changes, and adopting new tools.

To get started, go to the Comparisons tab and click Create Comparison. When setting up the comparison, select Before vs. after an event, which will allow you to define a new key event and select the groups that you want to compare.

If you have any questions or would like help setting up a time-based comparison, feel free to reach out to us at support@software.com.

Based on feedback from our community, we’ve improved our Features per Developer metric to better standardize industry benchmarks and provide better accuracy when comparing groups within your organization, such as developers using vs. not using GitHub Copilot.

As part of this update, the developer count will include contributors who created a pull request in the last 90 days. Previously, the developer count included any contributor who created or reviewed a pull request in the last 30 days.

Extending the activity window from 30 to 90 days accounts for seasonality, providing a more stable representation of your organization’s size over time. Developers who leave your organization are counted towards historical activity, but immediately removed from your developer count going forward.

Cost per feature still includes contributors who created or reviewed code in the last 90 days—to more precisely capture the costs associated with developing, reviewing, and delivering features. Development costs are calculated by multiplying a group’s cost per contributor by the count of developers and reviewers within the group.

You may notice small changes to your metrics and global benchmarks. You can read more about this change in our documentation, or feel free to reach out to us with any questions at support@software.com.

We’ve improved our feature calculations to automatically ignore pull requests in development workflows—such as dependency updates, back merges, and releases—that are not representative of developer productivity. These improvements have been requested by customers. As a result of this update, you may see a slight decrease in features per engineer, and industry benchmarks will be more accurate.

A pull request is excluded from your metrics if it meets any of the following criteria:

• It is authored by a bot (e.g. a dependency update)
• The head branch is the default branch (e.g. a back merge to sync changes from default into a development branch)
• It does not include any unique commits that haven’t already existed in a previous pull request (e.g. merging a release branch into main)
• More than three authors merged a pull request into the head branch (e.g. a long-lived feature branch)

You can review the list of pull requests that are included/excluded in our definitions by hovering over a data point and viewing more pull requests in the flyout.

Based on feedback from our community, we’ve made several improvements to our active engineers algorithm:

Improvements

• We’ve updated our active engineers definition to include users in your Git organization who have contributed or reviewed code in the last 30 days. Previously, only users who created pull requests were included. Users who exclusively review pull requests — such as team managers — will now be included in the active engineer count.

Bug fixes

• We’ve updated several queries to ensure that a 30-day rolling average of active engineers is always used. Previously, an active engineer may have been excluded if they did not create a pull request in a specific week.
• We’ve added better automatic detection for pull requests authored by bots, such as Dependabot updates. As a result, you may notice a decrease in your features per engineer metric. As a reminder, you can always manually filter out bots under Settings / Git Users.

Today we’re releasing a small but helpful improvement to our app for Slack. In our latest update, you can now send notifications — including daily and weekly digests — to private channels in your workspace.

To get started, you’ll need to approve new permissions for our Slack app (it takes just a few seconds). Head over to your team’s Connected Apps in your organization’s Settings and click Upgrade Slack.

⚡ How to send notifications

To send notifications to a private slack channel, you will need to first add our bot to the channel.

1. Select the channel where you want to send notifications.
2. In the message field, type “@Software.com” and hit send. Follow the prompts in Slack to invite our bot to this channel.
3. Visit your organization settings in the Software.com web app. In the Notifications section, refresh the dropdown menu and select your private channel.

You can learn more about our app in the Slack marketplace.