🤖 AI Summary
This work addresses the “intent gap” between user expectations and program behavior in AI-generated code by proposing intent formalization as a central pathway to transform informal requirements into verifiable formal specifications. We systematically identify intent formalization as a critical challenge for reliable coding in the AI era and introduce an end-to-end verifiable coding framework that integrates formal methods, test-driven development, AI-generated postconditions, domain-specific languages, and human-AI collaboration. The framework supports a spectrum of approaches ranging from lightweight testing to fully automated correctness-preserving synthesis. Preliminary experiments demonstrate that interactive, test-driven formalization effectively enhances program correctness, that AI-generated postconditions can uncover real-world bugs, and that provably correct code can be automatically synthesized from informal specifications.
📝 Abstract
Agentic AI systems can now generate code with remarkable fluency, but a fundamental question remains: \emph{does the generated code actually do what the user intended?} The gap between informal natural language requirements and precise program behavior -- the \emph{intent gap} -- has always plagued software engineering, but AI-generated code amplifies it to an unprecedented scale. This article argues that \textbf{intent formalization} -- the translation of informal user intent into a set of checkable formal specifications -- is the key challenge that will determine whether AI makes software more reliable or merely more abundant. Intent formalization offers a tradeoff spectrum suitable to the reliability needs of different contexts: from lightweight tests that disambiguate likely misinterpretations, through full functional specifications for formal verification, to domain-specific languages from which correct code is synthesized automatically. The central bottleneck is \emph{validating specifications}: since there is no oracle for specification correctness other than the user, we need semi-automated metrics that can assess specification quality with or without code, through lightweight user interaction and proxy artifacts such as tests. We survey early research that demonstrates the \emph{potential} of this approach: interactive test-driven formalization that improves program correctness, AI-generated postconditions that catch real-world bugs missed by prior methods, and end-to-end verified pipelines that produce provably correct code from informal specifications. We outline the open research challenges -- scaling beyond benchmarks, achieving compositionality over changes, metrics for validating specifications, handling rich logics, designing human-AI specification interactions -- that define a research agenda spanning AI, programming languages, formal methods, and human-computer interaction.