🐞 Analytics-Driven Continuous Improvement: Bug Resolution Project
April 03, 2024
By Ted SteinmannOverview
This project details a data-driven continuous improvement initiative aimed at increasing bug fix responsiveness and backlog health for a statewide EMS licensing platform. The effort was motivated by customer feedback and operational metrics indicating delays and aging unresolved issues.
Key Insight: Systematic measurement and algorithmic prioritization, not just more effort, drove sustainable improvement in bug resolution and customer trust.
Key Actions
- Conducted baseline analysis of bug backlog age, resolution time, and release cadence
- Curated backlog to remove obsolete and duplicate items
- Increased planned effort for bug fixes per sprint
- Shifted to monthly maintenance releases for faster customer impact
- Refined prioritization to account for issue staleness
Outcomes
- 14% reduction in total backlog size
- 40% reduction in average backlog age
- 41% reduction in age variability (standard deviation)
- 23% reduction in maximum backlog age
These improvements reflected system-level health gains, not just short-term metric movement, and were validated at our annual client conference in 2024.
Lessons Learned
- Backlog hygiene is as important as velocity—removing low-value items can be as impactful as fixing new ones.
- Frequent, predictable releases build customer trust and improve perception, even before all issues are resolved.
- Metrics need context—raw averages don’t always tell the full story, especially during intentional change.
Key Technologies
- Azure DevOps Boards & Queries (backlog management, data extraction, KPI tracking)
- Stability Rotation (algorithmic ranking for prioritization)
The algorithmic ranking calculated a score for each support item using the following factors:
| Factor | Description |
|---|---|
| Rank | Relative priority in the backlog |
| Tags | e.g. blocking, performance, security |
| Severity & Risk | Impact and urgency of the issue |
| Support Cases | Number of related support tickets |
| Staleness | Points per week old (issue age) |
- Algorithmic ranking scores were updated nightly using dynamic queries, ensuring prioritization always reflected the latest support signals and operational context. This enabled the team to consistently address the most impactful and urgent bugs.
- Python / Jupyter Notebooks (data wrangling, lifecycle analysis)
- Statistical Analysis (distribution analysis, variance reduction)
- Agile Delivery Practices (sprint planning, monthly release execution)
- Continue monthly reviews and backlog curation
- Refine prioritization based on customer impact
- Balance new development with sustained investment in stability
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