Prompting Is All You Need: Automated Android Bug Replay with Large Language Models (2306.01987v3)

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 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.

• The paper presents a novel approach that automates bug replay by extracting reproduction steps with LLMs and prompt engineering.
• It employs a two-phase methodology combining S2R entity extraction and guided GUI replay to simulate complex interactions.
• Evaluation results show an 81.3% bug reproduction rate, significantly reducing debugging time compared to traditional methods.

Summary and Implications of 'Prompting Is All You Need: Automated Android Bug Replay with LLMs'

The paper, "Prompting Is All You Need: Automated Android Bug Replay with LLMs," presents an innovative approach to tackle the challenge of automated bug replay within Android applications. The authors leverage the capabilities of LLMs to streamline the process of extracting bug reproduction steps from textual descriptions and subsequently executing these steps to recreate the bugs. They propose a methodology that eschews the traditional reliance on manually crafted heuristics and vocabulary lists, opting instead for LLMs guided by prompt engineering, few-shot learning, and chain-of-thought reasoning.

Key Components of the Approach

The approach is structured into two main phases:

1. S2R Entity Extraction: LLMs are employed to comprehend and extract steps to reproduce (S2R) entities, such as action types, target components, and input values, from bug reports. The LLMs are guided by prompt engineering—constituting a few-shot learning strategy with entity specifications and intermediate reasoning that mimics the thought process of a seasoned developer.
2. Guided Replay: This phase involves dynamically mapping the extracted entities to actions on the graphical user interface (GUI) of apps. A novel encoding scheme is used to translate GUI screens into a format understandable by LLMs. Through examples and detailed reasoning, the LLMs facilitate interaction with the GUI to simulate bug reproduction.

Evaluation and Results

The evaluation shows compelling outcomes, with the approach reproducing bugs from 81.3% of the selected Android bug reports. This represents a significant performance improvement over baseline methods such as ReCDroid and MaCa. Importantly, the paper also indicates substantial efficiency gains, reducing average bug replay time considerably compared to existing methods. The findings suggest that LLMs, equipped with thoughtful prompt engineering and contextual learning, can effectively understand and simulate complex bug reproduction tasks.

Implications for Software Engineering

The implications of this research are multifold. Practically, it offers a tool that could drastically reduce the manual effort developers exert in replaying bug reports, thereby accelerating the debugging and software maintenance cycle. Theoretically, it demonstrates the potential of LLMs in understanding and executing nuanced tasks in software engineering, paving the way for further applications of AI in domains requiring semantic understanding and context-aware reasoning.

Speculation on Future Developments

Given their success in complex task comprehension and execution, LLMs are likely to be further integrated into various aspects of software development workflows. Future advances may encompass multi-modal bug report analyses that consider not only text but also screenshots, stack traces, and logs. Additionally, human-AI collaboration in debugging, facilitated by interactive LLM prompts, could become a standard practice, where AI provides recommendations with confidence scores, allowing developers to make informed decisions.

In summary, this paper presents a significant stride in leveraging AI for automated bug replay, showcasing not only the efficacy of LLMs in understanding structured tasks but also underscoring the potential benefits such approaches hold for future software engineering practices.