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Q247

What Q247 is​

Q247 is a platform for measuring the effectiveness of software teams, built for the people who manage software delivery. It collects data passively, from the systems a team already uses every day.

Measurement is based on commits in code repositories, supplemented with events from Jira and Confluence. From those, Q247 calculates a consistent set of metrics and presents them on dashboards at the level of the organization, a project, a team and an individual person.

The first steps in the application are covered by Signing in and Navigation and profile, which describe the top bar, the user menu and the My profile page.

Rollout in four steps
  1. Creating the organization account in Q247.
  2. Choosing the deployment model and installing the Enterprise Plugin.
  3. Connecting repositories through a connector and assigning them to projects.
  4. Activating the people discovered during scanning, after which data appears on the dashboards.

The full order, together with the steps that are skipped most often, is described in Launching code analysis.

Main goals​

  • An objective, comparable view of the work of teams and of individual people.
  • Status conversations and project decisions based on data collected continuously.
  • Support for managing distributed, hybrid and external teams, where day to day observation of work is difficult.
  • An answer to the question about the return on investment in AI tools, based on actual usage.

Core capabilities​

Measuring contribution to code. Calories, Increments and Lines describe the amount of work done, and Retention checks whether the code written stays in the project.

Rhythm and regularity of work. Work Dynamics Indexes, Activity period and Workload show how work is distributed over time.

Flow of work through the process. Flow Metrics calculate Lead Time and Cycle Time from the history of ticket status changes in Jira.

Use of AI tools. AI Adoption combines telemetry from AI tools with commit recognition, broken down by projects, teams and people.

Interpretation of data in natural language. The AI Assistant describes the situation of a project, points out anomalies and suggests what is worth checking.

Five views with different levels of detail, described in Dashboards.

Who it is for​

  • Project managers and team leads
  • Technical directors and engineering managers
  • Teams responsible for work assessment and employee development
  • Agile coaches and delivery managers

Architecture​

Source code does not leave the infrastructure where the analysis runs. The analysis is performed by the Enterprise Plugin, which clones repositories locally and sends only the result to Q247. In the standard installation the plugin runs in the Client's infrastructure, so code never leaves their network. In the cloud variant the analysis runs in the Q247 infrastructure.

Integrations with Atlassian systems. Jira and Confluence deliver data as events, through webhooks, in both the cloud and the Data Center variant.

Identity and access. Signing in with a password and MFA, or through the organization's identity provider (SSO), with optional account provisioning through SCIM.

Work in the background. Scanning happens on the plugin side, with no impact on developer workstations.

The full specification for security and IT teams, including firewall rules and closed lists of the data transferred, is in Security.