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Validating AI contributions to scientific code rested largely on individual judgment, outside shared infrastructure for testing or review, and interfaces could support task-appropriate evaluation rather than leave it to the user.
The results show that RSEs most often improved code review by using pull requests, defining review expectations, adding lightweight process steps, and using tools and automation to reduce effort and increase consistency.
No detectable linear relationship exists between requirement properties and oracle accuracy in this dataset, suggesting that pretraining coverage and the semantic specificity of the required behavior dominate oracle correctness.
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