Leveraging Quality Intelligence and AI to Improve the Quality of Java Applications
summary
A case study shows a 20 million line Java payment platform plus ~1 million lines of custom rules, with 28% unit test coverage and 19% functional coverage (about 40% total), then uses treemap quality intelligence to target changed code, generates characterization tests, runs AI RAG workflows and MCP integration into IDEs, sees under 10% initial adoption until an MCP server reaches IntelliJ and then generates 1,000+ unit tests with mutation tests proving higher defect detection.
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chapters (17)
- 0:00Testing Java applications
- 1:04Company context
- 2:51Payments platform scale
- 3:31Quality engineering platform
- 4:52Visualizing codebase
- 6:16Test coverage gaps
- 8:45Characterization tests
- 10:29AI-driven unit testing
- 11:51AI workflows for tests
- 13:58Pilot results
- 14:39Developer adoption
- 15:43Integrating tools with MCP server for IDEs
- 16:24Lessons learned
- 16:45Open source tools
- 18:53MCP simplifies AI
- 19:56Custom AI workflows
- 20:39Q&A
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