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

Authors:

Chunyang ChenAuthors Info & Claims

ICSE '24: Proceedings of the 46th IEEE/ACM International Conference on Software Engineering

Article No.: 67, Pages 1 - 13

https://doi.org/10.1145/3597503.3608137

Published: 06 February 2024 Publication History

Abstract

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.

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Index Terms

Prompting Is All You Need: Automated Android Bug Replay with Large Language Models
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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DOI:10.1145/3597503

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Feng SLu HJiang JXiong THuang LLiang YLi XDeng YAleti AFilkov VRay BZhou M(2024)Enabling Cost-Effective UI Automation Testing with Retrieval-Based LLMs: A Case Study in WeChatProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695260(1973-1978)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695260
Cui DWang JWang QJi PQiao MZhao YHu JWang LLi QFilkov VRay BZhou M(2024)Three Heads Are Better Than One: Suggesting Move Method Refactoring Opportunities with Inter-class Code Entity Dependency Enhanced Hybrid Hypergraph Neural NetworkProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695068(745-757)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695068
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