Code-Switching (CS) is common in multilingual communication, yet it is unclear how well current Large Language Models (LLMs) reproduce naturally occurring switching patterns. This paper studies German–English CS (“Denglisch”) generated by GPT-4o and LLaMA-3.3, using Reddit data from the Denglisch Corpus as a reference. Model outputs are compared to authentic posts using established CS metrics (M-Index, I-Index, CESAR), an analysis of Shared Lexical Items (SLIs) as switch triggers, and a human evaluation of perceived naturalness and fluency. Both models approximate global CS characteristics but differ in the diversity and complexity in comparison to real data. LLaMA-3.3 more closely matches corpus-level metrics, whereas GPT-4o produces more conservative switching that is rated as significantly more natural and fluent. In addition, GPT-4o reproduces SLI-triggered switching patterns similar to those found in authentic data, while this effect is weaker for LLaMA-3.3.

LLMs are increasingly embedded in programming workflows, from code generation to automated code review. Yet, how gendered communication styles interact with LLM-assisted programming and code review remains underexplored. We present a mixed-methods pilot study examining whether gender-related linguistic differences in prompts influence code generation outcomes and code review decisions. Across three complementary studies, we analyze (i) collected real-world coding prompts, (ii) a user study, in which developers solve identical programming tasks with LLM assistance, and (iii) an LLM-based simulated evaluation framework that systematically varies gender-coded prompt styles and reviewer personas. We find that gender-related differences in prompting style are subtle but measurable, with female-authored prompts exhibiting more indirect and involved language, which does not translate into consistent gaps in functional correctness or static code quality. For LLM code review, in contrast, we observe systematic biases: on average, models approve female-authored code more, despite comparable quality. Our experiments show that gender-coded prompt styles influence code length and maintainability. We also observe substantial variation in reviewer behavior across models. Together, these findings suggest that fairness risks in LLM-assisted programming arise less from code generation accuracy than from LLM-based evaluation, particularly as LLMs are increasingly used as automated code reviewers.