# LLM for Test Script Generation and Migration: Challenges, Capabilities, and Opportunities

arXiv: https://arxiv.org/abs/2309.13574 · HTML: https://mlsanity.com/paper/2309.13574/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2023.

## Built on by (most cited first, 19 of 19)

- [Automatic Programming: Large Language Models and Beyond](https://mlsanity.com/paper/2405.02213/) (2024)
- [Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Tools](https://mlsanity.com/paper/2312.10622/) (2023)
- [Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests](https://mlsanity.com/paper/2408.11710/) (2024)
- [VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-Tuning](https://mlsanity.com/paper/2406.14056/) (2024)
- [NaviQAte: Functionality-Guided Web Application Navigation](https://mlsanity.com/paper/2409.10741/) (2024)
- [LLMLOOP: Improving LLM-Generated Code and Tests through Automated Iterative Feedback Loops](https://mlsanity.com/paper/2603.23613/) (2026)
- [Are Autonomous Web Agents Good Testers?](https://mlsanity.com/paper/2504.01495/) (2025)
- [GUITester: Enabling GUI Agents for Exploratory Defect Discovery](https://mlsanity.com/paper/2601.04500/) (2026)
- [LLMs for Test Input Generation for Semantic Caches](https://mlsanity.com/paper/2401.08138/) (2024)
- [From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper](https://mlsanity.com/paper/2503.07450/) (2025)
- [Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes](https://mlsanity.com/paper/2409.06416/) (2024)
- [Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach](https://mlsanity.com/paper/2412.06684/) (2024)
- [STAF: Leveraging LLMs for Automated Attack Tree-Based Security Test Generation](https://mlsanity.com/paper/2509.20190/) (2025)
- [Large Language Models for Software Testing: A Research Roadmap](https://mlsanity.com/paper/2509.25043/) (2025)
- [Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairing](https://mlsanity.com/paper/2312.10448/) (2023)
- [Evaluating ChatGPT-3.5 Efficiency in Solving Coding Problems of Different Complexity Levels: An Empirical Analysis](https://mlsanity.com/paper/2411.07529/) (2024)
- [FactFlow: Automatic Fact Sheet Generation and Customization from Tabular Dataset via AI Chain Design & Implementation](https://mlsanity.com/paper/2502.17909/) (2025)
- [Agent-Integrated Software: Interaction Contracts and Continuous Assurance](https://mlsanity.com/paper/2609.11381/) (2026)
- [SWaRL: Safeguard Code Watermarking via Reinforcement Learning](https://mlsanity.com/paper/2601.02602/) (2026)

## Abstract (arXiv)

This paper investigates the application of large language models (LLM) in the domain of mobile application test script generation. Test script generation is a vital component of software testing, enabling efficient and reliable automation of repetitive test tasks. However, existing generation approaches often encounter limitations, such as difficulties in accurately capturing and reproducing test scripts across diverse devices, platforms, and applications. These challenges arise due to differences in screen sizes, input modalities, platform behaviors, API inconsistencies, and application architectures. Overcoming these limitations is crucial for achieving robust and comprehensive test automation. By leveraging the capabilities of LLMs, we aim to address these challenges and explore its potential as a versatile tool for test automation. We investigate how well LLMs can adapt to diverse devices and systems while accurately capturing and generating test scripts. Additionally, we evaluate its cross-platform generation capabilities by assessing its ability to handle operating system variations and platform-specific behaviors. Furthermore, we explore the application of LLMs in cross-app migration, where it generates test scripts across different applications and software environments based on existing scripts. Throughout the investigation, we analyze its adaptability to various user interfaces, app architectures, and interaction patterns, ensuring accurate script generation and compatibility. The findings of this research contribute to the understanding of LLMs' capabilities in test automation. Ultimately, this research aims to enhance software testing practices, empowering app developers to achieve higher levels of software quality and development efficiency.