CitySpec: An Intelligent Assistant System for Requirement Specification in Smart Cities

TL;DR

CitySpec is an intelligent system that converts ambiguous city requirements into formal specifications, improving accuracy and adaptability for smart city monitoring systems.

Contribution

We introduce CitySpec, the first system to automatically translate city requirements into formal specs using a novel dataset, translation model, and online learning framework.

Findings

Requirement specification accuracy improved from 59.02% to 86.64%.

Strong adaptability demonstrated with F1 score increase from 77.6% to 93.75%.

Effective handling of diverse city requirements across domains.

Abstract

An increasing number of monitoring systems have been developed in smart cities to ensure that real-time operations of a city satisfy safety and performance requirements. However, many existing city requirements are written in English with missing, inaccurate, or ambiguous information. There is a high demand for assisting city policy makers in converting human-specified requirements to machine-understandable formal specifications for monitoring systems. To tackle this limitation, we build CitySpec, the first intelligent assistant system for requirement specification in smart cities. To create CitySpec, we first collect over 1,500 real-world city requirements across different domains from over 100 cities and extract city-specific knowledge to generate a dataset of city vocabulary with 3,061 words. We also build a translation model and enhance it through requirement synthesis and develop a novel online learning framework with validation under uncertainty. The evaluation results on real-world city requirements show that CitySpec increases the sentence-level accuracy of requirement specification from 59.02% to 86.64%, and has strong adaptability to a new city and a new domain (e.g., F1 score for requirements in Seattle increases from 77.6% to 93.75% with online learning).