Debug Smarter, Not Harder: AI Agents for Error Resolution in Computational Notebooks (2410.14393v1)

Abstract: Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these benefits come at the cost of reproducibility and an increased potential for bugs. With the rise of code-fluent LLMs empowered with agentic techniques, smart bug-fixing tools with a high level of autonomy have emerged. However, those tools are tuned for classical script programming and still struggle with non-linear computational notebooks. In this paper, we present an AI agent designed specifically for error resolution in a computational notebook. We have developed an agentic system capable of exploring a notebook environment by interacting with it -- similar to how a user would -- and integrated the system into the JetBrains service for collaborative data science called Datalore. We evaluate our approach against the pre-existing single-action solution by comparing costs and conducting a user study. Users rate the error resolution capabilities of the agentic system higher but experience difficulties with UI. We share the results of the study and consider them valuable for further improving user-agent collaboration.

• The paper introduces a stateful AI agent that iteratively interacts with computational notebooks to diagnose and resolve errors.
• It demonstrates superior debugging performance by reducing iterations required to fix errors and improving reproducibility.
• The study highlights user interface challenges and suggests future enhancements for more intuitive error resolution in collaborative environments.

Overview of "Debug Smarter, Not Harder: AI Agents for Error Resolution in Computational Notebooks"

The paper “Debug Smarter, Not Harder: AI Agents for Error Resolution in Computational Notebooks” presents a specialized AI agent system designed to address the unique challenges of error resolution in computational notebooks. Unlike traditional scripted programming environments, computational notebooks introduce stateful complexities that result in increased code entanglement and reproducibility issues. This paper discusses an AI-based system integrated into JetBrains Datalore, aimed at making the debugging process more efficient and effective.

Motivation and Problem Statement

Computational notebooks, while popular for tasks like data analysis and machine learning, suffer from reproducibility challenges and frequent bugs due to their inherently stateful and cell-based execution model. Traditional debugging tools, thriving in linear, script-based environments, fall short in these dynamic, interactive spaces. The authors point out that while LLMs—such as GPT-4 and Code Llama—demonstrate proficiency in code generation and error resolution, their application in computational notebooks is underexplored, primarily due to context size limitations and the stateful nature of these notebooks.

System Design

The authors propose an agentic system that employs a stateful AI agent capable of interacting with the computational notebook environment in a manner akin to a human user. This system is composed of three main components: an AI agent, the notebook environment, and a user interface. The architecture is designed to allow the agent to iteratively communicate with the environment, gather observations, and propose code edits to resolve errors. The agent utilizes function calls to interface with the notebook, supported by a memory stack to track interactions and a strategy inspired by reflection.

The selected LLM for this system is GPT-4-0613, chosen for its ability to handle function calls effectively. The agent's strategy includes guidelines to avoid non-meaningful fixes like code deletion and employs iterative interactions until the error is resolved or a predefined limit is reached.

Empirical Evaluation

The system's performance was evaluated through both quantitative cost analysis and qualitative user studies. The cost analysis revealed that while the AI agent's iterative approach resulted in higher input token consumption, the costs remain manageable. The AI agent’s error resolution capability was superior, often requiring only a few iterations to successfully solve errors.

The user study sheds light on user perceptions of the AI agent compared to a baseline single-action AI assistant. While the agentic system received higher ratings for its error resolution capabilities, it was noted for complexities in user interaction, indicating room for improvement in the user interface.

Implications and Future Work

This research contributes to the burgeoning field of AI-driven debugging tools, particularly in environments with complex execution states like computational notebooks. The introduction of an AI agent that can autonomously explore and act within a notebook environment represents a step toward more intelligent, context-aware development tools. However, the authors acknowledge limitations, particularly around the user interface and the necessity for enhanced user control and feedback mechanisms.

Future developments might focus on integrating more efficient information retrieval mechanisms, smaller AI models to reduce costs, and improved techniques for context management. Such advancements could facilitate broader adoption and elevate the role of AI assistants in resolving intricate software engineering challenges.

In conclusion, this paper positions itself as a significant contribution to AI-driven error resolution, offering actionable insights for both future research and practical application in collaborative data science platforms.