Statically Contextualizing Large Language Models with Typed Holes (2409.00921v1)

Abstract: LLMs have reshaped the landscape of program synthesis. However, contemporary LLM-based code completion systems often hallucinate broken code because they lack appropriate context, particularly when working with definitions not in the training data nor near the cursor. This paper demonstrates that tight integration with the type and binding structure of a language, as exposed by its language server, can address this contextualization problem in a token-efficient manner. In short, we contend that AIs need IDEs, too! In particular, we integrate LLM code generation into the Hazel live program sketching environment. The Hazel Language Server identifies the type and typing context of the hole being filled, even in the presence of errors, ensuring that a meaningful program sketch is always available. This allows prompting with codebase-wide contextual information not lexically local to the cursor, nor necessarily in the same file, but that is likely to be semantically local to the developer's goal. Completions synthesized by the LLM are then iteratively refined via further dialog with the language server. To evaluate these techniques, we introduce MVUBench, a dataset of model-view-update (MVU) web applications. These applications serve as challenge problems due to their reliance on application-specific data structures. We find that contextualization with type definitions is particularly impactful. After introducing our ideas in the context of Hazel we duplicate our techniques and port MVUBench to TypeScript in order to validate the applicability of these methods to higher-resource languages. Finally, we outline ChatLSP, a conservative extension to the Language Server Protocol (LSP) that language servers can implement to expose capabilities that AI code completion systems of various designs can use to incorporate static context when generating prompts for an LLM.

• The paper introduces a novel method for integrating static type context to enhance LLM-based code completion.
• It details a static retrieval mechanism that incorporates type definitions and function headers to enrich code context.
• Iterative error correction validated by the MVUBench benchmark shows significant accuracy improvements, notably for lower-resource languages.

Statically Contextualizing LLMs with Typed Holes

In the paper "Statically Contextualizing LLMs with Typed Holes," researchers from the University of Michigan propose a novel approach to address a significant problem faced by contemporary LLM-based code completion systems: the inability to generate correct code without appropriate context. The authors argue that better integration with the type and binding structure of the programming languages, facilitated by language servers, can significantly enhance the performance of these systems.

The paper introduces a methodology where LLM code generation is integrated into a programming environment like Hazel, which features total syntax and type error recovery via automatic hole insertion. This ensures that the environment is always in a semantically meaningful state, even in the presence of incomplete code with holes. The authors propose that this approach allows for the generation of code completions informed by a deeper understanding of the entire codebase context, rather than merely the cursor's immediate surroundings.

Core Contributions

1. Static Retrieval: The authors propose a static retrieval mechanism where the language server determines the type and typing context at the cursor and retrieves relevant type definitions and function headers from the entire codebase. This context is then included in the prompt provided to the LLM.
2. Syntactic and Static Error Correction: To further refine the completions generated by the LLM, the authors implement a mechanism where the generated code is analyzed for any syntax and type errors. These errors are then fed back into the model, prompting it to correct any mistakes iteratively over multiple rounds.
3. MVUBench: To evaluate their approach, the authors introduce MVUBench, a benchmark suite consisting of various model-view-update (MVU) web applications. This benchmark suite is designed to be free from data contamination issues and easily portable across different programming languages, ensuring a fair evaluation of the proposed techniques.
4. ChatLSP: The paper outlines a prospective extension to the Language Server Protocol (LSP) named ChatLSP. This extension includes additional methods to support the retrieval of static information necessary for proper contextualization in LLM-based code completions.

Results and Implications

The researchers conduct extensive experiments using both GPT-4 and StarCoder2-15B models, evaluating their performance across the MVUBench tasks. The results show a significant improvement in code completion accuracy when static context from the language server is included in the prompt. The inclusion of type definitions alone greatly improves performance, while the combination of type definitions and relevant function headers provides the most substantial boost. Iterative error correction further enhances the correctness of the generated code.

A notable finding is the difference in effectiveness of these techniques between high-resource languages like TypeScript and lower-resource languages like Hazel. While TypeScript benefitted from the additional context, Hazel showed a more pronounced improvement, highlighting the potential of the proposed approach for lesser-known languages.

The paper's methodological rigor and extensive evaluation suggest several implications for the future of AI-driven code completion systems:

1. Enhanced Developer Productivity: By providing more accurate and contextually relevant code completions, developers can save time and cognitive resources, leading to increased productivity.
2. Broader Applicability: While the experiments primarily focus on Hazel and TypeScript, the techniques introduced are broadly applicable to any language with a rich type and binding discipline.
3. Future Developments: The ChatLSP extension provides a pathway for future language servers to support these advanced contextualization techniques, fostering further integration of AI with modern IDEs.

In conclusion, the paper "Statically Contextualizing LLMs with Typed Holes" offers a significant advancement in the field of AI-driven code completion. By leveraging the static semantics of programming languages and tightly integrating with language servers, the proposed approach overcomes many of the limitations of current LLM-based systems. The comprehensive evaluation and introduction of MVUBench provide a strong foundation for future research and development in this area, potentially transforming how developers interact with and benefit from AI in their coding environments.