Unclear bug triage delays releases and overloads teams
A software company with 3,000 employees develops 900 features a year. Every month, 4,500 bug tickets come in, and triaging them by hand takes days.
- Technology
- Software Development
Key metric
Time spent on bug triage
320145h
Monthly hours spent on bug triage, compared
At a glance
Starting point
A software company in the technology sector with around 3,000 employees develops about 900 new features a year across several product lines. Development is organized in around 20 cross-functional squads that share a central issue tracking system. On average, 4,500 new bug tickets are logged every month, about 3,600 of them from customer reports and 900 from internal testing. Support and QA handle the initial intake, while rotating duty roles in development assign tickets to teams and components. That adds up to about 250 new bugs per working day, which are reviewed, assessed and distributed in several triage rounds.
- 4,500
- Bug tickets per month
- 20
- Development teams
The challenge
Manual bug triage costs the development teams a lot of time and leads to inconsistent decisions. Every week, senior developers together spend around 80 hours reading, assessing and routing tickets to the right teams. Many bugs come with unstructured descriptions, screenshots or log excerpts that first have to be interpreted. Duplicate reports are often spotted too late, so several teams work on similar problems in parallel. Critical bugs get lost in the noise, while loud but less urgent tickets get priority. Release planning suffers from unreliable numbers on open critical defects.
Typical symptoms in the company
Why this remained unsolved
The problem had been known for a long time but was seen as an unavoidable part of everyday development. Earlier improvement attempts focused on stricter ticket templates and extra mandatory fields, which burdened reporters and barely improved the quality of descriptions. Purely rule-based automation failed because bugs are described in very different ways and key clues sit in free text, logs or attachments. A larger IT project just for triage seemed hard to justify, even though the total time lost was clearly felt.
Our solution
The company introduced an agentic workflow that structures bug triage around the existing issue tracker. New tickets are analyzed automatically, matched against similar cases and enriched with suggestions for priority, affected components and responsible teams. The workflow combines information from the ticket text, attachments, logs and version data from the repositories. For each ticket, the system creates a structured triage profile with a short description, likely impact, affected services and possible duplicates. Duty roles in development review these suggestions in a focused interface, confirm or correct them and release the ticket to the teams’ backlogs with one click. Feedback flows back into the workflow and gradually improves the suggestions.
Where generative AI comes in
Generative AI analyzes free-text descriptions, log excerpts and screenshots, condenses them into a concise problem statement and derives suggestions for priority, affected components and responsible teams. It detects duplicates through semantic similarity and delivers usable suggestions that duty roles only need to review and adjust.
- Automatic analysis of new bug tickets, extracting context, affected functions and likely impact from free text and attachments.
- Semantic duplicate detection that groups and merges similar bugs across titles, descriptions, logs and history.
- Suggestion logic for priority and routing that derives components, services and teams from code history, ownership and previous tickets.
- Triage profiles in a focused interface where duty roles review, correct and approve suggestions with one click.
- Feedback loops that use confirmed and corrected suggestions to gradually improve future triage decisions.
Rollout in clear steps
Current-state analysis
Survey of ticket volumes, triage processes and roles involved, selection of pilot teams and definition of the key priority categories.
Data connection
Connecting the issue tracker, reading historical tickets, linking to repositories and ownership data, defining the routing targets.
Modeling
Configuring the agentic logic for analysis, duplicate detection and routing, trained on the pilot teams’ historical triage decisions.
Pilot operation
Introducing the triage assistant in two pilot teams, running it alongside manual triage, comparing decisions and fine-tuning.
Rollout
Gradual rollout to all development teams, adjusting duty roles, training and monitoring the effects on lead times and quality.
Measured: effects after six months
Period: effects after six months.
| Metric | Value | Basis |
|---|---|---|
| Time spent on bug triageKey metric | Reduced by around 55 percent | Time tracking before/aftermeasured |
| Lead time until team assignment | from 2 days to 6 hours | System measurementmeasured |
| Corrections to automatic suggestionsQuality measure | around 18 percent of tickets | Sampleestimated |
Cost-effectiveness
The solution delivers its full benefit mainly where ownership structures are stable and component fields are well maintained. During major organizational changes or with frequently changing teams, the need for manual control increases.
Integration and operations
Transferability
The agentic workflow for bug triage can be transferred to other contexts where many unstructured reports have to be assessed, prioritized and distributed to teams. What matters is a central system for cases and historical decisions that can serve as a learning base. Industries with complex products, many variants and frequent changes benefit most, especially where experienced staff have so far spent a lot of time sorting and assigning tickets or reports.
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Briefly describe your process. We’ll get back to you with an initial assessment of whether the pattern can be transferred, and what it realistically takes.