The Quality of Claude AI-authored Python Tests Is Not Weaker Than Human-authored Tests

Abstract:We evaluate the quality of Claude AI-written Python tests against human-written Python tests from two established open-source projects Django and Pandas. Hundreds of tests per corpus are scored under one identical protocol. Using one-sided non-inferiority bounds, we find that the tests written by recent Claude models (Sonnet/Opus 4.6 and later) are no weaker than the two human-written corpora. In this study: (i) the AI-written corpus is tests from real tools, not synthetic tests generated in isolation against a fixed target, the setup used by every other AI-test-generation study we are aware of; (ii) every test is individually scored under three independent fault-injection protocols plus a seven-axis qualitative design rubric, allowing methods to cross-validate each other; (iii) tests are scored individually, rather than suite-level, identifying exactly which specific tests need attention.

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

arXiv:2608.15188 [cs.SE]

(or arXiv:2608.15188v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2608.15188 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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