Mining Type Constructs Using Patterns in AI-Generated Code

• URL: http://arxiv.org/abs/2602.17955v1
• Date: Fri, 20 Feb 2026 03:17:42 GMT
• Title: Mining Type Constructs Using Patterns in AI-Generated Code
• Authors: Imgyeong Lee, Tayyib Ul Hassan, Abram Hindle,
• Abstract summary: It remains unstudied whether AI essentially outperforms humans in type-related programming tasks.<n>We present the first empirical analysis to answer these questions in the domain of TypeScript projects.<n>Surprisingly, even with all these issues, Agentic pull requests have 1.8x higher acceptance rates compared to humans for TypeScript.
• Score: 1.2107297090229683
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Artificial Intelligence (AI) increasingly automates various parts of the software development tasks. Although AI has enhanced the productivity of development tasks, it remains unstudied whether AI essentially outperforms humans in type-related programming tasks, such as employing type constructs properly for type safety, during its tasks. Moreover, there is no systematic study that evaluates whether AI agents overuse or misuse the type constructs under the complicated type systems to the same extent as humans. In this study, we present the first empirical analysis to answer these questions in the domain of TypeScript projects. Our findings show that, in contrast to humans, AI agents are 9x more prone to use the 'any' keyword. In addition, we observed that AI agents use advanced type constructs, including those that ignore type checks, more often compared to humans. Surprisingly, even with all these issues, Agentic pull requests (PRs) have 1.8x higher acceptance rates compared to humans for TypeScript. We encourage software developers to carefully confirm the type safety of their codebases whenever they coordinate with AI agents in the development process.

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