Automated Data Model Generation From Textual Specifications: A Case Study of ECHONET Lite Specification

Abstract

ECHONET Lite stands as a leading protocol for smart home appliances in Japan, and the publication of data models plays a critical role in fostering collaboration with other ecosystems, not just for ECHONET Lite but for any protocol or standard. Typically, data models are meticulously crafted by experts through the arduous task of condensing and summarizing extensive specification documents. As an illustration, generating a data model solely for the ECHONET Lite protocol (without incorporating other protocols) can demand thousands of working hours. This paper presents an AI-driven solution aimed at alleviating the burden of laborious, repetitive tasks prone to errors during the creation of data models from ECHONET Lite specifications, automating these processes to save human effort. The proposed solution employs Natural Language Processing techniques to extract key vocabularies from natural language descriptions of the specification, mirroring the approach of experts. Consequently, this solution can generate several data models for the ECHONET Lite protocol in a matter of seconds, all using a standard laptop. The findings indicate that machines are capable of emulating experts in extracting vocabularies, ensuring both syntactic error-free outcomes and consistency in the generated data models. Furthermore, the machine offers rapid, dependable results and enhances the reusability of exported data models across various platforms. The generated data models meet the same requirements as those created by humans. This solution is integrated into an official workflow for generating data models for the ECHONET Lite web API and others.

Keywords

• Data models generation
• ECHONET device objects
• ECHONET lite web API
• machine-generated data models