Here is a clean, professional presentation-style version suitable for an email, slide notes, or an internal update:
Leveraging Modern Tooling for Local Profiling of COMET Applications
Technology is evolving rapidly and today enables capabilities that were previously difficult to achieve without significant investment in tooling, infrastructure, and engineering effort.
While working on enabling QFFBridge to run in the local environment, I noticed that more than half of the runtime options were dedicated to capturing heap dumps and profiling data. This indicated a strong need for runtime diagnostics and performance visibility.
Using the Byte Buddy library (available through Citi’s Artifactory), I implemented a Gradle-based profiling solution written entirely in Groovy. This solution:
- Builds a lightweight profiling agent automatically
- Attaches to a locally started QFFBridge instance
- Requires no modifications to the existing application code
- Collects runtime execution statistics during application runs
After execution completes, a Gradle reporting task generates a profiling report summarizing where the application spent its time during execution.
This significantly improves our ability to analyze the behavior of COMET applications running locally and provides insights into runtime performance.
Current Status
For now:
- Only the QFFBridge local run startup changes have been committed.
- The profiling capability remains experimental and is available for evaluation.
However, the same approach could easily be extended to:
- Other COMET applications running locally
- Potentially even QA environments on Linux
This would allow us to collect runtime statistics and identify where code execution spends the most time.
Example Use Case
Consider the recent code review investigating GUI slowdowns.
By attaching this lightweight profiling agent to the running application, we could quickly:
- Identify performance bottlenecks
- Pinpoint specific classes or methods responsible for delays
- Provide concrete evidence during performance investigations
While architectural improvements—such as moving complex flows to background asynchronous execution and providing immediate user feedback—remain an effective long-term solution (a strategy successfully applied at Bank of America prior to my return to Citigroup), having profiling visibility dramatically accelerates diagnosis and troubleshooting.
Next Step
Please review the attached profiling report.
I propose scheduling a short demonstration to show how the profiling agent works and to discuss whether this capability would be useful in addressing current COMET performance challenges.
If you want, I can also turn this into a 1-slide executive presentation (much stronger for management) with a diagram showing: Gradle → ByteBuddy agent → COMET runtime → profiling report.
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