Software engineering metrics

Software engineering metrics.
All in one dashboard.

Pull requests, issues and DORA, collected automatically from your Git and project tools. See where work waits, by team and project, with nothing to enter.

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ProjectPayments ▾
Last 90 days ▾Weekly ▾
DORA MetricsValue streamPull requestsProcess
Pull requests cycle time breakdown
1d 9hrs−12%
Based on 612 merged pull requests
Coding 10h +4%Pickup 14h +62%Review 9h −31%
Pull request size
9.4+18%
Avg files changed / PR
Work in progress (WIP)
14.2+9%
Avg in progress / week
Illustrative data
What you track

See how work flows.Column by column.

Axify reads your Jira, Azure DevOps or GitLab board and follows every issue, from active work to done. The same definitions for every team, with nothing to enter.

Work distribution

The share of bugs, stories and tasks in what the team completes, week after week.

Issue distribution
21%
Of completed items are bugs
StoryBugTaskImprovement

Cycle time by status

Time spent in each column of your board, so you see where issues wait.

Cycle time breakdown
4d 2hrs−11%
Average cycle time
Dev QAIn ProgressIn Review

Service level expectation

How long it takes to finish 85% of issues or PRs. A realistic promise, based on your history.

Service level expectation
6 days−14%
85% of items finish in 6 days or less

Workflow stability

Issues started compared to issues completed. When the gap grows, work in progress piles up.

Workflow stability
148started139completed
Items started and completed per week
Items startedItems completed
Illustrative data
Glossary

Engineering metrics,explained.

What each metric measures in Axify, and how it’s calculated.

PULL REQUESTS
PR cycle time
From first commit to merge. Splits into coding, pick-up and review time.
Pick-up time
From review request to first review. A high value means PRs are waiting.
Review time
From first review to merge.
PR size
Files changed per PR. Smaller PRs get reviewed faster.
Self-reviewed PRs
PRs merged without a review from someone else, a risk signal.
ISSUES AND FLOW
Issue cycle time
From active work to done, with the time spent in each board column.
Throughput
PRs merged or issues completed per period, and by issue type (bug, story…).
Work in progress (WIP)
PRs or issues still open at the end of the period.
Service level expectation (SLE)
The time within which 85% of issues or PRs finish, based on your history.
Workflow stability
Issues started compared to issues completed. A growing gap means work in progress is piling up.
DORA
Deployment frequency
Production deployments per period.
Lead time for changes
From first commit to production deployment.
Change failure rate
The share of deployments that caused an incident.
Failed deployment recovery time
Average time to resolve an incident tied to a deployment.

Further reading: Cycle time · Lead time for changes · Software development KPIs · Developer productivity metrics

Axify Intelligence

A number moves.Find out why.

Compare with the previous period, open the issues and pull requests behind each metric, then ask Axify Intelligence, in the app or from Claude, ChatGPT and Cursor through the MCP server.

Axify Intelligence
“Why did issue cycle time go up in the Payments project?” Issues now spend 1d 2h in Dev QA, up from 9h last month. Six issues are waiting there at once, while the other columns are stable.
Axify Intelligence → MCP server →
Process
Project: Payments ▾Last 30 days ▾
StatusAvg timeChangeIssues
Ready14h−4%3
In Progress1d 6h+2%5
In Review18h−10%4
Dev QA1d 2h+189%6
Illustrative data
How it works

Connected in minutes.In three steps.

STEP 01

Connect your tools

GitHub, GitLab, Bitbucket and Azure DevOps for code, Jira, Azure DevOps or GitLab for issues. Read-only.

GitHubGitLabBitbucketAzure DevOpsJira
STEP 02

Map your workflow

Map your board columns once, then add deployments and incidents through webhooks or New Relic.

STEP 03

Explore and act

Break down by team, project or contributor, compare periods and see whether your changes move the numbers.

Customer results

Teams that ship faster.

51%

Up to 51% faster delivery in 3 months

The Business Development Bank of Canada (BDC) cut its delivery time by shifting QA left and working in smaller stories.

Read the case study →
22X

22X more deliveries in 5 months

With 60% shorter PR cycle time and 63% shorter lead time for changes.

Read the case study →
Related features

Go further.

DORA METRICS

The four DORA metrics

Delivery to production in detail, by team and performance level.

Explore DORA metrics →
VALUE STREAM MAPPING

See where work waits

Delivery lead time split by stage, from planning to production.

Explore value stream mapping →
DEVELOPER PRODUCTIVITY

Productivity and AI

How AI coding tools move throughput and cycle time.

Explore developer productivity →

Frequently asked questions

What are software engineering metrics?
They measure how work flows through a development team: how fast code moves through review and into production, how much work gets delivered, how much is in progress and how stable deployments are. They show where work waits, not how to rank people.
Which metrics does Axify track?
For pull requests: cycle time and its phases (coding, pick-up, review), throughput, PR size, comments per review, self-reviewed PRs and work in progress. For issues: cycle time by board column, throughput by type, issues started, work in progress, workflow stability and service level expectation (SLE). Plus the four DORA metrics.
Where does the data come from? Do we need to enter anything?
Nothing to enter. Axify reads GitHub, GitLab, Bitbucket and Azure DevOps for code, Jira, Azure DevOps or GitLab for issues, and New Relic or webhooks for deployments and incidents. Every integration is read-only.
How are engineering metrics different from DORA metrics?
DORA metrics measure delivery to production with four numbers. Engineering metrics add what happens before that: pull requests, code review and issue flow. Axify shows both in the same dashboard.
Can we break metrics down by team or project?
Yes. Every metric breaks down by team, project, project group and contributor, by day, week or month, compared to the previous period. You can also open the list of pull requests, issues and deployments behind each number.
Does Axify measure lines of code or code quality?
No. Axify measures flow and delivery outcomes, not lines of code or a code quality score.
Does Axify read our source code?
No. Axify doesn’t clone repositories or read file contents. It reads metadata about pull requests, reviews, issues and deployments.
Which plan includes it?
Engineering metrics are included in the Pro and Enterprise plans. Every plan starts with a 14-day free trial, no credit card required.

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