The dynamically evolving and technologically-driven hybrid landscape of transportation networks integrated with advanced edge computing capabilities has demonstrated efficient communication and computation techniques to guarantee robust quality of services (QoS) to vehicles. However, conventional communication systems in the Internet of Vehicles (IoV) still encounter challenges in providing meaningful low-latency communication and AI-assisted real-time synchronization on the edge. One reason is that it has exhausted the Shannon limit by utilizing cellular, NOMA, and Wi-Fi technologies. Therefore, we present an integrated approach leveraging Semantic Communication (SC), and Digital Twin (DT) deployment to tackle the challenges caused by high-dimensional data exchanges and resource spectrum crunch leading to inevitable latency constraints. SC stimulates meaningful transmission of data to high-mobility vehicles by providing a relevant knowledge base (KB) and DT deployment. In this paper, we established the vehicular SC (VSC) model, and DT deployment strategy. We formulate a multi-objective optimization problem (MOP) to maximize the overall QoS of the system by jointly optimizing VSC and DT deployment. Compared to traditional deep-reinforcement learning (DRL) schemes, we propose a Digital Twin Semantic Sensing using the Multi-vehicle DRL ($DTS^{2}$-MVDL) algorithm which addresses the MOP and persistent issues of multi-dimensional, continuous, and discrete nature of the vehicular environment. Lastly, we employ age of Information (AoI), latency, and QoS as the performance metrics to determine the algorithmic efficiency.

Architectural tactics such as heartbeat, resource pooling, and scheduling provide solutions to satisfy reliability, security, performance, and other critical characteristics of a software system. Current design practices advocate rigorous up-front analysis of the system's quality concerns to identify tactics and where in the code they should be used. In this paper, we explore a bottom-up approach to recommend architectural tactics based on latent topics discovered in the source code of projects. We present a recommender system developed by building predictor models which capture relationships between topical concepts in source code and the use of specific architectural tactics in that code. Based on an extensive analysis of over 116,000 open source systems, we identify significant correlations between latent topics in source code and the usage of architectural tactics. We use this information to construct a predictor for generating tactic recommendations. Our approach is validated through a series of experiments which demonstrate the ability to generate package-level tactic recommendations. We provide further validation via two large-scale studies of Apache Hive and Hadoop to illustrate that our recommender system predicts tactics that are actually implemented by developers in later releases.

Software datasets play a crucial role in advancing automated software traceability research. They can be used by researchers in different ways to develop or validate new automated approaches. The diversity and quality of the datasets within a research community have a significant impact on the accuracy, generalizability, and reproducibility of the results and consequently on the usefulness and practicality of the techniques under study. Collecting and assessing the quality of such datasets are not trivial tasks and have been reported as an obstacle by many researchers in the domain of software engineering. This paper presents a first-of-its-kind study to review and assess the datasets that have been used in software traceability research over the last fifteen years. It presents and articulates the current status of these datasets, their characteristics, and their threats to validity. Furthermore, this paper introduces a Traceability-Dataset Quality Assessment (T-DQA) framework to categorize software traceability datasets and assist researchers to select appropriate datasets for their research based on different characteristics of the datasets and the context in which those datasets will be used.