A digital twin is a virtual representation of a physical asset that serves as a pivotal convergence technology that facilitates real-time prediction, optimization, monitoring, control, and improved decision-making. It can be widely applied to various domains, such as automotive, manufacturing, logistics, and smart cities. The automotive industry, in particular, is actively integrating digital twins throughout the product life cycle, from research and development, production, sales, and services to enhance the overall customer experience. This paper presents insights and lessons learned on software practice and experience related to implementing smart mobility digital twins, focusing on the potential of transportation digital twins built from data collected by electric vehicles (EVs) with EV edge cloud and automated machine learning (AutoML). Despite current limitations in data sufficiency, we forecast that, as the SDV trend accelerates and the adoption of EVs increases, the digital twin will become essential for the intelligent transportation system (ITS) in future smart cities, enabling accurate traffic predictions even in areas with limited road infrastructure. The successful integration of real-time data, high-performance prediction models, and automated service environments will enhance the effectiveness toward an SDV edge-empowered transportation digital twin.

Abstract Background The importance of software in maritime transportation is rapidly increasing as the industry seeks to develop and utilize innovative future ships, which can be realized using software technology. Due to the safety‐critical nature of ships, software quality assurance (SQA) has become an essential prerequisite for such development. Objective Based on the unique characteristics of the maritime domain, the purpose of this study was to achieve effective SQA resource allocation to reduce post‐release quality costs. Method Software defect prediction (SDP) is employed to predict defects in newly developed software based on models trained with past software defects and to update information using machine learning. This study demonstrated that just‐in‐time SDP is applicable to maritime domain practice and can reduce post‐release quality costs via combination with an estimation model, qCOPLIMO. Results Using real‐world datasets collected from the maritime industry, performance and cost‐benefit analyses of SDP were performed. A successful model was obtained that meets the performance criterion of 0.75 in within‐project defect prediction (WPDP) but not cross‐project defect prediction (CPDP). In addition, the cost‐benefit analysis results showed that 20% effort enables the detection of 56% of defects on average and that the post‐release quality cost can be reduced by 37.3% in the maritime domain. Conclusion SDP can be successfully applied to the maritime domain. Further, it is desirable to utilize WPDP instead of CPDP once minimum high‐quality commits are available that can be identified as defective or not. Finally, SDP can help reduce review effort and post‐release quality costs.