The 6 best tools to deduplicate and triage bug reports in 2026
Every bug tracker fills up with duplicates. One survey puts the average at 12% of bug reports, and for some large projects the rate is far higher. Each duplicate costs a triage pass, and unmerged duplicates hide how many people are actually hitting a problem.
The useful question is which kind of duplicate a tool catches. Crash dedupe (same stack trace, grouped mechanically) is a solved problem. The hard kind is human-written reports: a Slack message, a Zendesk ticket, and a GitHub issue describing the same bug in three different vocabularies. Disclosure: Modem is ours. Every other entry is described from its public documentation.
The short version
| Tool | What it dedupes | Where reports come from |
|---|---|---|
| Modem | Human reports across channels | Slack, Discord, support tools, email, GitHub |
| Sentry | Crashes and errors, by stack trace + ML | Your app's error events |
| Linear | Issues inside the tracker | Linear's triage inbox |
| Jira | Issues inside the tracker | Jira projects |
| Shake | In-app bug reports | Its mobile/web SDK |
| IrisAgent | Support tickets | Your support desk |
1. Modem
Modem dedupes the human-written kind, before reports ever become tracker issues. It captures bug reports from Slack, Discord, support tools, email, and GitHub, and its triage layer matches new reports against existing topics semantically, so "checkout spinner never stops" and "payment page hangs forever" end up on the same topic with both reporters attached.
That last part is the point of deduping: the merged topic lists every affected customer, which feeds its AI-assigned priority and tells you who to follow up with when the fix merges. The matching holds across channels because reports land in a context graph that also connects identities: the person who filed the GitHub issue and pinged you in Slack counts as one reporter, not two.
Where it fits: teams whose bug reports arrive as conversation across several channels, ahead of the tracker. Where it doesn't: crash and error dedupe from your app's own telemetry. That's Sentry's job, and Modem sits alongside it rather than replacing it.
2. Sentry
Sentry groups error events into issues by stack trace, and its ML-based grouping compares new errors against existing issues semantically; Sentry reported the upgrade prevented 20% more duplicate issues while halving incorrect merges. Its Seer agent extends this into native AI triage for error tracking.
Where it fits: dedupe of machine-generated error reports. If your duplicates are crashes, start here; nothing conversational competes on this ground.
3. Linear
Linear's triage inbox is where new issues land for review, and its Triage Intelligence is one of the native AI triage features that shipped across major trackers, flagging likely duplicates as issues arrive.
The boundary is the tracker itself: Linear can only dedupe what became a Linear issue. Reports that stayed in Slack or a support queue never reach the inbox. (If that's your gap, see our guide to turning feedback into Linear issues.)
Where it fits: engineering teams already routing all bug reports into Linear triage.
4. Jira
Jira's duplicate detection flags semantically similar issues even when reports are worded differently, and Atlassian's Rovo brings broader AI triage to Jira projects. The Atlassian ecosystem also supports custom duplicate detectors built without code.
Same boundary as Linear: it dedupes what's in Jira, not what's still in a support thread.
Where it fits: Jira shops with high issue volume and multiple reporting teams.
5. Shake
Shake is an in-app bug reporting SDK with an AI triage layer over the reports it collects: classification, duplicate detection, and routing happen on reports submitted through its widget, with device context attached automatically.
Dedupe quality benefits from the structured capture; the trade-off is that only reports entering through Shake are covered.
Where it fits: mobile-first teams that want structured in-app reports deduped at the point of capture.
6. IrisAgent
IrisAgent works the support-desk side: it automates triage of bug reports arriving as tickets, correlating customer tickets with known engineering issues so support isn't filing a fresh bug for each customer who hits an existing one.
Where it fits: support orgs drowning in tickets that map to a small set of known bugs.
How to choose
Sort your duplicates by origin. If they're crashes, use Sentry and stop reading. If they're issues already inside your tracker, the native detection in Linear or Jira is the low-effort fix. If your duplicates are born earlier, as messages in Slack, tickets, and community threads that each become a separate issue, you need dedupe at the capture layer: Modem across channels, Shake for in-app reports, IrisAgent for the support desk. Most teams have two of these problems at once; the tools stack rather than compete. For the broader triage picture, see our guide to AI triage tools for engineering teams.