Technical Debt Prioritisation - Identifying & Fixing Highest ROI Issues
summary
Shows concrete ways to shrink an overwhelming tech-debt backlog: visualize hotspots with SonarQube, CodeScene and a homegrown C3 tool, correlate coverage/complexity/churn to find a 242-line file with low coverage and high churn, and use logs to surface APIs that drive 80% of requests so fixes hit high impact. Demonstrates metrics (158 day feature cycle, flow efficiency waits), timeboxed experiments, A/B refactor-or-rewrite decisions, and databases (AWR), with examples of what worked and how progress is validated.
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chapters (13)
- 0:00Introduction
- 2:18How do we know we have Technical Debt
- 3:41Visualisation
- 8:21About Naresh Jain
- 15:03Visualise
- 16:28Sonar
- 18:30C3 Tool
- 20:46Performance Tool
- 25:27Prioritisation
- 28:08Using Sonar
- 29:10Techniques for Prioritisation
- 30:21Continuous Evolution
- 36:49Questions
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