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An increasing number of monitoring systems have been developed in smartcities to ensure that real-time operations of a city satisfy safety andperformance requirements. However, many existing city requirements are writtenin English with missing, inaccurate, or ambiguous information. There is a highdemand for assisting city policy makers in converting human-specifiedrequirements to machine-understandable formal specifications for monitoringsystems. To tackle this limitation, we build CitySpec, the first intelligentassistant system for requirement specification in smart cities. To createCitySpec, we first collect over 1,500 real-world city requirements acrossdifferent domains from over 100 cities and extract city-specific knowledge togenerate a dataset of city vocabulary with 3,061 words. We also build atranslation model and enhance it through requirement synthesis and develop anovel online learning framework with validation under uncertainty. Theevaluation results on real-world city requirements show that CitySpec increasesthe 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 forrequirements in Seattle increases from 77.6% to 93.75% with online learning).

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