arXiv subjects

Andrew Blinn

Publications and source records attributed to Andrew Blinn.

2 recordsLinked to original sources

arXiv2025

Syntactic Completions with Material Obligations

Code editors provide essential services that help developers understand, navigate, and modify programs. However, these services often fail in the presence of syntax errors. Existing syntax error recovery techniques, like panic mode and multi-option repairs, are either too coarse, e.g. in deleting large swathes of code, or lead to a proliferation of possible completions. This paper introduces $\texttt{tall}~\texttt{tylr}$, an error-handling parser and editor generator that completes malformed code with $\textit{syntactic obligations}$ that abstract over many possible completions. These obligations generalize the familiar notion of holes in structure editors to cover missing operands, operators, delimiters, and sort transitions. $\texttt{tall}~\texttt{tylr}$ is backed by a novel theory of tile-based parsing, conceptually organized around a $\textit{molder}$ that turns tokens into tiles and a $\textit{melder}$ that completes and parses tiles into terms using an error-handling generalization of operator-precedence parsing. We formalize melding as a parsing calculus, $\textsf{meldr}$, that completes input tiles with additional obligations such that it can be parsed into a well-formed term, with success guaranteed over all inputs. We further describe how $\texttt{tall}~\texttt{tylr}$ implements molding and completion-ranking using the principle of $\textit{minimizing obligations}$. Obligations offer a useful way to scaffold internal program representations, but in $\texttt{tall}~\texttt{tylr}$ we go further to investigate the potential of $\textit{materializing}$ these obligations visually to the programmer. We conduct a user study to evaluate the extent to which an editor like $\texttt{tall}~\texttt{tylr}$ that materializes syntactic obligations might be usable and useful, finding both points of positivity and interesting new avenues for future work.

cs.PL↗

arXiv2024

Statically Contextualizing Large Language Models with Typed Holes

Large language models (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.

cs.PL↗