# Prompting Is All You Need: Automated Android Bug Replay with Large Language Models

arXiv: https://arxiv.org/abs/2306.01987 · HTML: https://mlsanity.com/paper/2306.01987/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2023.

## Built on by (most cited first, 30 of 40)

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- [Prompting and Fine-tuning Large Language Models for Automated Code Review Comment Generation](https://mlsanity.com/paper/2411.10129/) (2024)
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- [TransitGPT: A Generative AI-based framework for interacting with GTFS data using Large Language Models](https://mlsanity.com/paper/2412.06831/) (2024)
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- [Agents in the Sandbox: End-to-End Crash Bug Reproduction for Minecraft](https://mlsanity.com/paper/2503.20036/) (2025)
- [Generative transformations and patterns in LLM-native approaches for software verification and falsification](https://mlsanity.com/paper/2404.09384/) (2024)
- [Are Human Rules Necessary? Generating Reusable APIs with CoT Reasoning and In-Context Learning](https://mlsanity.com/paper/2405.03509/) (2024)
- [A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality](https://mlsanity.com/paper/2408.00435/) (2024)
- [LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models](https://mlsanity.com/paper/2409.01909/) (2024)
- [Combining Language and App UI Analysis for the Automated Assessment of Bug Reproduction Steps](https://mlsanity.com/paper/2502.04251/) (2025)
- [Automatically Generating Rules of Malicious Software Packages via Large Language Model](https://mlsanity.com/paper/2504.17198/) (2025)
- [Program Analysis Guided LLM Agent for Proof-of-Concept Generation](https://mlsanity.com/paper/2604.07624/) (2026)

## Abstract (arXiv)

Bug reports are vital for software maintenance that allow users to inform developers of the problems encountered while using the software. As such, researchers have committed considerable resources toward automating bug replay to expedite the process of software maintenance. Nonetheless, the success of current automated approaches is largely dictated by the characteristics and quality of bug reports, as they are constrained by the limitations of manually-crafted patterns and pre-defined vocabulary lists. Inspired by the success of Large Language Models (LLMs) in natural language understanding, we propose AdbGPT, a new lightweight approach to automatically reproduce the bugs from bug reports through prompt engineering, without any training and hard-coding effort. AdbGPT leverages few-shot learning and chain-of-thought reasoning to elicit human knowledge and logical reasoning from LLMs to accomplish the bug replay in a manner similar to a developer. Our evaluations demonstrate the effectiveness and efficiency of our AdbGPT to reproduce 81.3% of bug reports in 253.6 seconds, outperforming the state-of-the-art baselines and ablation studies. We also conduct a small-scale user study to confirm the usefulness of AdbGPT in enhancing developers' bug replay capabilities.