Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation

New research reveals a simple but powerful principle for getting better code from AI assistants: keep your tests close. A study by Éric Jacopin, accepted at AIware 2026, investigated how the structure of test code—specifically whether tests are inline with the implementation (co-located) or in separate blocks—affects the quality of code generated by foundation models. The large-scale empirical study analyzed over 830 generated files from 12 different models across three major providers, evaluating them on Determinism, Preservation, and Correctness using the SEGA framework.

The findings are stark. When models like GPT-4, Claude 3, and others were prompted with code using inline test syntax (exemplified by Python doctests), they achieved near-perfect preservation (100%) and correctness rates between 92% and 100%. In contrast, using separated test syntax (like Rust's #[test] attribute) exposed massive performance gaps between model tiers, with correctness plummeting to 0% for some models while others hit 100%. A mechanistic analysis of seven open-source architectures, including six transformers and the RWKV-6 RNN, showed that inline test markers received 2.8 to 4.4 times stronger attention in five of the seven models. This suggests the model's 'focus' is directly influenced by test placement.

The study's core conclusion is that in the era of AI coding assistants, test syntax is no longer just a philosophical choice but a concrete software design decision with measurable impact on output quality. Co-locating tests with implementation code acts as a stronger, more contextual signal for the model, leading to more reliable and correct code generation. This design recommendation appears robust, as the positive effect of co-location was also observed in the non-transformer RWKV-6 architecture, indicating it may hold for future model designs.