OpenAI shares case of automating software maintenance using GPT-6

OpenAI has disclosed on its blog a case in which it automated internal software maintenance using the coding agent Codex and GPT-6 family models.

The company said development sped up thanks to the coding agent, increasing the maintenance burden. It said there was more work deleting unused code and cleaning up and fixing completed tests.

OpenAI teams then gave GPT-6 Astra short and vague tasks, asking it to find needed materials on Slack or the experiment platform Statsig and clean up unused code.

Astra reviewed past pull requests, Slack conversations, internal project records and performance data from production environments to select code to delete. It also explained the reasons for deletion. Five engineers incorporated more than 1,400 pull requests over two weeks while handling other tasks. More than 800,000 lines of code were deleted. One service cut its container image size by 48 percent to 1.72 GB from 3.31 GB.

OpenAI also deployed Astra to clean up more demanding old experiments. Astra assessed CPU usage by experiment and found sources of waste that no one had known about for years. The team deleted hundreds of experiments and feature flags.

Astra confirmed that one flag marked as "permanent" used a lot of CPU and requested human review. After the review, the team deleted the flag and cut CPU usage on major services by about 0.42 percent.

The team also tested the same work with the cheaper GPT-6.1 Sol. After checking 6,000 files and 1 million lines of code, Sol set to the highest reasoning intensity found 131 issues, or 92 percent of the 142 issues Astra found. API costs were $15.12, or 14 percent of Astra's $109.06.

The company also highlighted improvements in continuous integration, or CI. After incorporating about 30 pull requests based on Codex suggestions, the median CI completion time fell to about 4.5 minutes from about 10 minutes. Runtime for a bundle of 63 tests dropped 95 percent to 29.8 seconds from 592.6 seconds.

The company advised starting with small maintenance tasks, reviewing results and then expanding the scope.