Keypup Blog - Page 2 | Keypup Blog Articles

Engineering Metrics 12 min read

The Hot Potato Problem: How Ticket Reassignment Churn Inflates Cycle Time

Why do some Jira tickets bounce between engineers and teams for weeks before anyone fixes them? Learn what engineering leaders on Reddit say about ticket reassignment churn at enterprise scale, and how Keypup MCP uses previous assignees, assignment dates, issue types, projects and linked pull request authors to measure how often tickets change hands, what each hand-off costs in cycle time, who actually wrote the code and which open tickets are being passed around right now.

Liam Davis

Engineering Metrics 13 min read

6 Best Engineering Analytics Tools for Jira and Git (2026)

Compare the 6 best engineering analytics tools for Jira and Git in 2026 — Keypup, LinearB, Jellyfish, Swarmia, Faros AI and Pluralsight Flow — across Git and Jira unification, DORA Metrics and cycle time, dashboard customization, AI and natural language, integration breadth, setup speed and security. Find out which platform turns your code and ticketing data into one reporting layer with prescriptive recommendations.

Liam Davis

Engineering Metrics 13 min read

The Review Queue: Why Pull Requests Spend More Time Waiting Than Being Worked On

Why do pull requests sit for days before anyone reviews them? Learn what engineering leaders on Reddit say about code review bottlenecks at enterprise scale, and how Keypup MCP uses review request dates, first review dates, approvals, reviewers and lines changed to measure your wait for first review, break cycle time into phases, find when requests get stuck, show who carries the review load and list the pull requests waiting right now.

Stephane Ibos

Engineering Metrics 13 min read

The Hotfix Treadmill: How Emergency Fixes Quietly Eat Enterprise Delivery Capacity

Why do some engineering organizations spend every Friday shipping hotfixes? Learn what engineering leaders on Reddit say about hotfixes, reverts and firefighting at enterprise scale, and how Keypup MCP uses pull request branches, labels, titles, approvals, merge times and linked Jira issues to measure your emergency fix rate, when and where it runs, how it relates to skipped reviews and which unreviewed fixes need a second look.

Arnaud Lachaume

Engineering Metrics 13 min read

The 90% Done Epic: How Scope Creep Quietly Breaks Enterprise Roadmap Forecasts

Why does every epic sit at 90% done for weeks? Learn what engineering leaders on Reddit say about scope creep, moving goalposts and missed roadmap dates at enterprise scale, and how Keypup MCP uses Jira epics, their child issues and due dates to measure how much scope is added after planning, where it comes from, where it concentrates and which "almost done" epics are still growing.

Thomas Williams

Engineering Metrics 13 min read

The Always-On Tax: How After-Hours Work Signals Engineering Burnout

Velocity looks fine, until your best engineers quit. Learn what engineering leaders on Reddit say about after-hours commits, weekend work and burnout at enterprise scale, and how Keypup MCP uses commit, review and merge timestamps to measure how much work happens outside working hours, where it concentrates, who is overloaded without naming anyone, and what it costs in skipped reviews.

Liam Davis

Engineering Metrics 13 min read

Merge Hell: How Long-Lived Branches Quietly Slow Down Enterprise Delivery

Your median cycle time looks healthy, yet every release still ends in merge conflicts, rebases and big-bang reviews. Learn what engineering leaders on Reddit say about long-lived branches and the trunk-based vs GitFlow debate at enterprise scale, and how Keypup MCP uses pull request dates, target branches, commits, reviews and mergeability to measure branch lifetime, find where branches live longest and land aging work before it drifts into conflict.

Stephane Ibos

Engineering Metrics 14 min read

6 Best Engineering Metrics Tools for Benchmarking (2026)

Compare the 6 best engineering metrics tools for multi-team benchmarking in 2026 — Keypup, LinearB, Swarmia, Jellyfish, Faros and Appfire Flow — on cross-team benchmarking, org-level rollups, DORA Metrics, Git-to-ticketing linking, custom KPIs, AI capabilities and data security. See how to compare teams on equal terms and find where your delivery process is broken.

Liam Davis

Engineering Metrics 13 min read

The PR Graveyard: How Much Engineering Work Dies Before It Merges?

In large engineering organizations, thousands of pull requests are closed without merging or left to go stale every year, and nobody measures the cost. Learn what engineering leaders on Reddit say about abandoned pull requests, and how Keypup MCP uses pull request state, review, size, mergeability and Jira link data to size the PR graveyard, find where work dies, and triage stale pull requests before they are lost.

Arnaud Lachaume

Engineering Metrics 13 min read

The Ramp-Up Tax: Why Nobody Can Measure How Long New Engineers Take to Become Productive

Enterprises hire hundreds of engineers a year, but almost none can say how long it takes them to ship at full speed, where they get stuck, or whether onboarding is improving. Learn what engineering leaders on Reddit say about the onboarding ramp-up tax, and how Keypup MCP uses pull request, review and Jira data to measure time to first merge, ramp-up curves, review friction and onboarding load.

Thomas Williams

Engineering Analytics & AI ROI 16 min read

Is AI Actually Improving Your SDLC? How to Measure the Impact of AI Coding Assistants on Delivery and Code Quality

Are your engineers really using AI for code, is it making delivery faster, and is quality holding up? A three-question method, with the data and queries to answer each one.

Arnaud Lachaume

Engineering Metrics 12 min read

The KTLO Tax: Why You Can't Prove How Much Engineering Goes to Bugs and Maintenance

Leadership asks why the roadmap is slipping. Engineering says "bugs and maintenance", but can't prove how much. Learn what engineering leaders on Reddit say about the keep-the-lights-on (KTLO) tax in large organizations, why investment allocation is so hard to measure, and how Keypup MCP uses Jira issue types, labels and linked pull requests to show where engineering capacity actually goes.

Liam Davis