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Automatic Documentation of Java Code with Formal Specifications using Artificial Intelligence.

descriptionPublicationkeyboard_double_arrow_right Conference object 01 Jan 2025 English
Authors: Recio, Juan Carlos; Saborido Infantes, Rubén; Chicano-García, José-Francisco;

handle: 10630/39949 , 11705/JISBD/2025/90

Automatic Documentation of Java Code with Formal Specifications using Artificial Intelligence.

Abstract

We present a system that produces JML (Java Modeling Language) formal specifications of Java programming language code using Higher Order Logic (HOL). The system verifies that the formal specifications are correct using the Isabelle/HOL theorem prover assistant. Our approach combines automated unit test case generation, invariant detection, conversion between formal languages, and specialized Large Language Models (LLMs) for proof inference and formalized code documentation generation. This novel pipeline bridges the gap between practical software engineering and formal verification methods, enabling developers to automatically produce trustworthy documentation backed by mathematical proofs and formal statements that can later be combined in a modularized way to prove the correctness of large software systems.

Short paper submitted and accepted to the AI4SE track at JISBD 2025

Ministerio de Ciencia, Innovación y Universidades

Ministerio de Ciencia e Innovación (MICINN)

Keywords

Métodos formales (Informática), Formal verification, Large Language Models, Proceso en lenguaje natural (Informática), JML, Invariant detection, Isabelle/HOL, Diseño de sistemas, Inteligencia artificial

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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