The average age of the explored models is 1.5 years. They discuss PHP 8.3, which was released in 2023 and has not been actively supported since 2025. Agentic coding isn't used.
I'm not sure what the point is of publishing results on the mid-2025 state of the art in October 2026.
> We study a common intermediate setting: a developer pastes one complete file into a chat assistant [5], which sees the file’s internal structure but not its callers or dependencies. We evaluate the first answer, not an agent’s result after iterating on errors.
Very unsurprising results given this methodology. The agentic approach where models are allowed to iterate on changes is a very big part of what has made LLM coding tools actually useful.
> We study a common intermediate setting: a developer pastes one complete file into a chat assistant [5], which sees the file’s internal structure but not its callers or dependencies.
Ack! Do people actually try to develop software this way?
I do it from time to time when I’ve run out of tokens at the end of the month. But even then I’ve created a script to pack multiple code files and their dependencies into a single txt, with a tree overview.
And of course you never change the test suite in the process… That would be bad developer practice, LLM or not.
Gemini 3.1 Pro can easily work with this effectively, if not a bit slower than an agent.
Not any more, but it was common before Claude Code came along (~18 months ago). Based on the model versions used it may have still been the most common thing when the paper was started.
One year ago I was very skeptical about such attempts. Today, it would be my first choice as LLMs evolved a lot and the result of carefully using a LLM for this task can be very, very good.
The average age of the explored models is 1.5 years. They discuss PHP 8.3, which was released in 2023 and has not been actively supported since 2025. Agentic coding isn't used.
I'm not sure what the point is of publishing results on the mid-2025 state of the art in October 2026.
> We study a common intermediate setting: a developer pastes one complete file into a chat assistant [5], which sees the file’s internal structure but not its callers or dependencies. We evaluate the first answer, not an agent’s result after iterating on errors.
Very unsurprising results given this methodology. The agentic approach where models are allowed to iterate on changes is a very big part of what has made LLM coding tools actually useful.
> We study a common intermediate setting: a developer pastes one complete file into a chat assistant [5], which sees the file’s internal structure but not its callers or dependencies.
Ack! Do people actually try to develop software this way?
I do it from time to time when I’ve run out of tokens at the end of the month. But even then I’ve created a script to pack multiple code files and their dependencies into a single txt, with a tree overview.
And of course you never change the test suite in the process… That would be bad developer practice, LLM or not.
Gemini 3.1 Pro can easily work with this effectively, if not a bit slower than an agent.
Not any more, but it was common before Claude Code came along (~18 months ago). Based on the model versions used it may have still been the most common thing when the paper was started.
Models:
- Claude Sonnet
- Gemini 2.5 Flash
- Gemini 2.5 Pro
- GPT-5 Codex
- Llama 3.3 70B
It's inconvenient how quickly things are improving, makes potentially-interesting results like this dubiously relevant
One year ago I was very skeptical about such attempts. Today, it would be my first choice as LLMs evolved a lot and the result of carefully using a LLM for this task can be very, very good.