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

Éric Jacopin

Keywords: AI-assisted development, code generation, test infrastructure, mechanistic interpretability, software design

TL;DR: Co-locating tests with implementation code produces measurably better AI-generated code across 12 models and 7 architectures.

Abstract:

AI coding assistants increasingly generate code alongside tests. How developers structure test code, whether inline with the implementation or in separate blocks, has traditionally been a matter of testing philosophy. We investigate whether this choice affects AI code generation quality.

We conduct a large-scale empirical study (830+ generated files, 12 models, 3 providers) using SEGA, a three-dimensional evaluation framework measuring Determinism, Preservation, and Correctness. Comparing inline test syntax (Python doctests) against separated test syntax (Rust \texttt{\#[test]} blocks) on a d-ary heap implementation, we find that: (1) inline tests yield near-perfect preservation (100\%) and correctness (92--100\%) across all models; (2) separated tests expose stark model-tier gaps (0--100\% correctness) and independence between preservation and correctness; (3) model behavior evolves across generations, and notably one model breaks the test suppression pattern of its three predecessors; (4) mechanistic analysis on 7 open-source architectures (6 transformers and a gated-linear Recurrent Neural Network (RNN)) reveals inline test markers receive 2.8--4.4$\times$ stronger attention in 5/7 models, with causal validation via knockout and steering experiments; the co-location mechanism extends to a non-transformer architecture, suggesting the design recommendation is robust to future architectural shifts. In the Foundation Model era, test syntax structure is a software design concern: co-locating tests with implementation code produces measurably better AI-generated code.

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Paper Type: Full-length papers (i.e. case studies, theoretical, applied research papers). 8 pages

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Submission Number: 23