Release order is a graph problem most teams solve in a spreadsheet and a Slack thread. ArcRelease builds the graph from the links your teams already maintain, then enforces it at deploy time.
ArcRelease reads the blocked-by and relates-to links your teams already keep in Jira, turns them into a directed dependency graph, and enforces that graph at deploy time — so an out-of-order deployment is refused rather than discovered afterwards.
The dependency list lives in a spreadsheet one person maintains, and it is stale by the second day.
Every planning call opens with people reading their tickets aloud to rebuild an order that already existed in the Jira links nobody joined up.
We found the hard dependency after we shipped, when the downstream service fell over.
You do not draw a dependency graph from an empty canvas. ArcRelease suggests edges from your existing Jira links and you correct the few that are wrong — then it holds the line at deploy time.
ArcRelease ingests Jira issues, fix versions and blocked-by links and proposes a first-pass dependency graph.
You start from a populated graph and correct the handful of suggestions that are wrong, rather than drawing one by hand.
Hard dependencies refuse an out-of-order deploy; conflicts across releases surface at planning.
The graph updates as the links change, so it never drifts into the spreadsheet-on-day-two problem.
ArcRelease reads from these tools and is never in the deploy path. Nothing in your pipeline changes.
No. The graph starts from the Jira links your teams already maintain. You correct the suggestions rather than build from scratch, which is why most edges are accepted rather than authored.
Only if you wire the advisory check into branch protection. ArcRelease is not in the deploy path; most teams run advisory first and enforce later once they trust the graph.
You can add edges manually, and ArcRelease also infers candidates from shared services and deployment history. The Jira links are the fastest starting point, not the only source.
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