Original title: Baseline Output Length for Prioritizing Visible-Test Regressions Under a Task-Count Budget: Controlled Model and Retrieval-Context Changes in LLM-Enabled Code Generation
Gyumin Nam, Geunseok Yang · September 2026
When parts of an AI coding system change, like its underlying model or the extra text it retrieves, programs can break. Fixing this requires testing tasks again to find errors. But running every single test costs time, so teams often have a budget to test only a few tasks. Researchers looked for a simple way to decide which tasks to check first without needing any new training. They tested sorting tasks by their baseline output length, which is the average number of characters in earlier answers. They found that this length-based ordering beat random guessing when changing retrieval contexts. A test swapping the model was promising but less certain. However, when multiple big system shifts happened at once, the approach failed to meet its performance goal.
You are a teacher with a stack of homework to grade. You only have time to spot-check a few assignments for mistakes. You notice that students who wrote very long answers on the previous assignment tend to make more mistakes on the current one. In this paper, the AI's past response length is used to decide which code generation tasks to re-examine after the AI's model or context changes.
When an AI system that writes code gets updated, its outputs can change in unexpected ways. Developers have limited resources to re-run all tests, so they need to know which ones to check first. This paper shows that simply using the length of previous outputs can help prioritize effectively, saving time and catching bugs faster.
The compound-shift block did not meet the prespecified criterion (APFD 0.597 below 0.60). The model-swap estimate was imprecise. A bridging experiment with only three regression events could not isolate the contribution of the task population. The budget is defined as task re-executions, not token, latency, or total operational cost.
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Baseline Output Length for Prioritizing Visible-Test Regressions Under a Task-Count Budget: Controlled Model and Retrieval-Context Changes in LLM-Enabled Code Generation — Gyumin Nam, Geunseok Yang — Electronics — doi:10.3390/electronics15184334
Abstract licence: CC BY
Explanation generated by Synthesis from the abstract — it is not the original paper.