This thesis investigated the effectiveness of natural language processing models

in automating test generation for Flutter applications that use Bluetooth com-

munication, an aspect of cyber-physical systems that remains underexplored.

The study evaluated four open-source natural language processing-based code

generation models: StarCoder, GPT-NeoX, CodeGen, and CodeT5, focusing

on their ability to generate end-to-end and integration tests. A structured

experimental methodology was used to assess each model’s output across three

levels of prompt complexity. The results showed that while models such as

StarCoder demonstrate some logical structure, none of the models produced

fully functional tests without manual intervention. Edge case handling, such as

unstable connections and device compatibility, proved particularly challenging.

The findings highlight the current limitations of small-scale natural language

processing models in cyber-physical system testing scenarios and emphasize the

need for more advanced models, improved prompt strategies, and domain-specific

fine-tuning to close the performance gap between human and machine-generated

tests.