Engineering

Article | 2022-04-27

Corrigendum to ‘‘Contemplation on China’s Energy-Development Strategies and Initiatives in the Context of its Carbon Neutrality Goal” [Engineering 7 (2021) 1684–1687]

Yinao Su , Houliang Dai et al.

Article | 2022-04-27

Advanced Antennas Push Forward Wireless Connectivity

Kwai Man Luk , Baoyan Duan

Article | 2022-05-27

Integrated Development of Rail Transit and Energies in China: Development Paths and Strategies

Rail transit features high levels of energy consumption and carbon emission; therefore, transforming its energy structure and developing a novel rail transit energy system with self-consistent energy supply become significant approaches for realizing carbon peak and neutrality in China. In this article, we first review the demand for the integrated development of rail transit and energies, summarize the current status and development trends of the integration, and analyze natural endowments for the integration in terms of solar and wind resources. Subsequently, based on the characteristics of electrified and non-electrified rail transits, critical technology paths are proposed considering the natural endowments of renewable energies. Moreover, based on the assessment of self-consistent supply potentials of new energies, a series of scenarios and methods are introduced. A roadmap and suggestions are proposed for rail-energy integration development, aiming to a self-consistent energy system construct for rail transit. The suggestions include: (1) encouraging technology innovation regarding green and intelligent rail transit to form a technology system for rail-energy integration; (2) implementing major scientific and technological projects to coordinate the industrial layout of new energy and rail transit; and (3) formulating support policies to create a policy guarantee system for green finance.

Jia Limin , Cheng Peng et al.

Article | 2022-05-26

Industrial Development of Hydrogen Blending in Natural Gas Pipelines in China

The development of hydrogen industry is crucial for realizing the green and low-carbon transformation of terminal energy consumption. The efficiency of hydrogen transportation is key to the development of hydrogen industry. Blending hydrogen in natural gas pipelines can improve the scale and efficiency of hydrogen distribution in a short period of time, and it provides a solution for expanding the scale of hydrogen application. Based on defining the industrial chain of hydrogen blending in natural gas pipelines, the paper discusses the values of developing the blending industry in terms of promoting the hydrogen industry, resolving renewable energy consumption, ensuring energy supply security, realizing the deep carbon reduction of terminal energy consumption, and encouraging energy technology innovation. Moreover, the paper summarizes the international progress and domestic current status of the blending industry. It unravels key issues regarding the hydrogen blending proportion, adaptability of pipes and terminal equipment, and their safety use and technical economy. Furthermore, we propose the following suggestions: (1) strengthening the toplevel design, (2) building a standards system for safety supervision as well as technology and operation management of hydrogen blending in natural gas pipelines, (3) actively deploying demonstration projects through multi-participation, and (4) exploring diversified application scenarios and business models, thereby cultivating a sustainable industrial ecosystem to steadily promote the scaled development of the industry.

Zhong Bing , Zhang Xuexiu et al.

Editorial | 2022-05-20

Intelligent analysis for software data: research and applications

Over the last few decades, software has been one of the primary drivers of economic growth in the world. Human life depends on reliable software; therefore, the software production process (i.e., software design, development, testing, and maintenance) becomes one of the most important factors to ensure the quality of software. During the production process, large amounts of software data (e.g., source code, bug reports, logs, and user reviews) are generated.With the increase in the complexity of software, how to use software data to improve the performance and efficiency of software production has become a challenge for software developers and researchers. To address this challenge, researchers have used information retrieval, data mining, and machine learning technologies to implement a series of automated tools to improve the efficiency of some important software engineering tasks, such as code search, code summarization, severity/priority prediction, bug localization, and program repair. However, these traditional approaches cannot deeply capture the semantic relations of contextual information and usually ignore the structural information of source code. Therefore, there is still room to improve the performance of these automated software engineering tasks.The word “intelligent” means that we can use a new generation of artificial intelligence (AI) technologies (e.g., deep learning) to design a series of “smart” automated tools to improve the effectiveness and efficiency of software engineering tasks so that developers’ workloads are dramatically reduced.Currently, advancement has been achieved by a new generation of AI approaches, which are well suited to address software engineering problems. We show two classical and popular automated software engineering tasks using “intelligent” analysis technology for software data as follows:1. Intelligent software developmentCode search and summarization can help developers develop quality software and improve efficiency. Code search is a frequent activity in software development that can help developers find suitable code snippets to complete software projects. Developers usually input the descriptions of these snippets as queries to achieve this purpose. However, it is extremely challenging to design a practically useful code search tool. The previous information retrieval based approaches ignored the semantic relationship between the high-level descriptions expressed by natural language and low-level source code, which affects the performance of code search. Different from information retrieval based methods, deep learning technologies can automatically learn feature representations and build mapping relationships between inputs and outputs. Therefore, the performance of code search is improved. Code summarization is the task of automatically generating natural language descriptions of source code, which can help developers understand and maintain software. In traditional automated code summarization work, researchers tend to use the summary template to extract keywords of source code, which ignores the grammar information of source code. At present, neural network technology has developed vigorously. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and other deep learning networks are applied to the task of automated code summarization.2. Intelligent software maintenanceSeverity/Priority prediction can automatically recommend appropriate labels to help developers reduce the workload for labeling severity and priority levels, which are the important features of bug reports. Severity shows the serious levels of the reported bugs, while priority indicates which bugs should be first fixed. The prediction task can help developers quickly assign the important bugs to appropriate developers for fixing them so that the efficiency of software maintenance is improved. Traditional approaches usually adopt machine learning technologies such as support vector machine (SVM) and naive Bayes (NB) to predict the severity/priority level. However, these approaches cannot overcome the problem of data imbalance, so the prediction accuracy is not perfect. Some deep learning technologies, such as CNNs and graph convolutional networks (GCNs), can effectively resolve this problem and capture the contextual semantic information of bug reports so that the prediction performance is improved.In this context, we organize a special feature in the journal on intelligent analysis for software data. This special feature covers software architecture recovery, app review analysis, integration testing, software project management, defect prediction, and method rename, as well as related applications. After a rigorous review process, six papers were selected.

Tao ZHANG , Xiaobing SUN et al.

Research Article | 2022-05-20

An incremental software architecture recovery technique driven by code changes

It is difficult to keep software architecture up to date with s during . Inconsistency is caused by the limitations of standard development specifications and human power resources, which may impact software maintenance. To solve this problem, we propose an incremental software (ISAR) technique. Our technique obtains dependency information from changed code blocks and identifies different strength-level dependencies. Then, we use double classifiers to recover the architecture based on the method of mapping code-level changes to architecture-level updates. ISAR is evaluated on 10 open-source projects, and the results show that it performs more effectively and efficiently than the compared techniques. We also find that the impact of low-quality architectural documentation on effectiveness remains stable during .

Li WANG , Xianglong KONG et al.

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