# Testing Java Memory Management with Chronicle-FIX using AI

DevFeed: [Testing Java Memory Management with Chronicle-FIX using AI](<https://devfeed.tech/articles/testing-java-memory-management-with-chronicle-fix-using-ai-30760.md>)

Original publisher: [Read original article](<http://blog.vanillajava.blog/2026/06/testing-java-memory-management-with.html>)

Author: Peter Lawrey (noreply@blogger.com)

Published: 2026-06-05T09:03:26Z

Content type: article

Language: en

Sources: [Vanilla Java](<https://devfeed.tech/sources/vanilla-java.md>)

Topics: [Java](<https://devfeed.tech/topics/java.md>), [Testing](<https://devfeed.tech/topics/testing.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [claude](<https://devfeed.tech/tags/claude.md>), [code](<https://devfeed.tech/tags/code.md>), [codex](<https://devfeed.tech/tags/codex.md>), [gc](<https://devfeed.tech/tags/gc.md>), [java](<https://devfeed.tech/tags/java.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [opinion](<https://devfeed.tech/tags/opinion.md>), [testing](<https://devfeed.tech/tags/testing.md>), [ubuntu](<https://devfeed.tech/tags/ubuntu.md>), [ubuntu-24-04](<https://devfeed.tech/tags/ubuntu-24-04.md>)

## AI overview

This article reports on using Codex to create and test a JLBH benchmark for Chronicle-FIX from documentation and sample code. On a Java 25.0.2 system running Ubuntu, the benchmark measured half-round-trip latency of 2.4 to 3.7 microseconds, with about 11 microseconds at the 99.999th percentile under Parallel GC. The author presents AI as useful for benchmark code and experimentation, while cautioning that business logic generally requires substantial human authorship or rewriting.

## Source excerpt

While I am sceptical of using AI for release code, it has plenty of uses that previously weren't practical, such as determining how easy your software is to use. If an AI can "figure it out" with a few hints, then you are on the right track. For me, the value of AI is what you learn using it. For more Techincal Information on Chronicle-FIX What AI Does Well and What It Doesn't Claude and Codex are effective for producing idiomatic code; for low-latency code, it needs a significant body of example code. In this case, it was able to utilise sample code for benchmarks. If it was being used to write business logic, it would need the code to be mostly complete examples, and then it could write variations on that. If you were starting, it would be better to either; a) get it to write something functionally correct with the expectation you would rewrite it again manually, or b) write the code yourself and use AI to assist you in improving it. The AI Benchmark Trial I gave Codex (GPT-5.5) the task of writing a JLBH benchmark for Chronicle-FIX from documentation and sample code, testing the round-trip latency of W -> D and D -> 8 messages. The throughput is 50K/s each way. The W market data message is ~512 bytes, and the D new order signal and '8' execution reports are a small ~160 bytes. The test is run for 15 minutes each. I verified the benchmark was written but avoided hand-tuning it; then I asked it to trial different GC options, expecting they wouldn't make much difference, since the application is low GC; however, there might still be some difference. The system is using Java 25.0.2 on a Ryzen 9 9955HX3D with 64 GiB of RAM in a laptop running Ubuntu 24.04.04 LTS. A significant difference between JMH and JLBH benchmark harness is that JLBH supports many concurrent asyncrhonous inflight actions whereas JMH tests one action at a time. The Results The half-round-trip time (RTT/2) was between 2.4 and 3.7 microseconds (< 0.004 milliseconds). For the recommended Parallel GC, t