ACM Computing Surveys最新文献

Every software system undergoes changes, for example, to add new features, fix bugs, or refactor code. The importance of understanding software changes has been widely recognized, resulting in various techniques and studies, for instance, on change-impact analysis or classifying developers’ activities. Since changes are triggered by developers’ intentions—something they plan or want to change in the system, many researchers have studied intentions behind changes. While there appears to be a consensus among software-engineering researchers and practitioners that knowing the intentions behind software changes is important, it is not clear how developers can actually benefit from this knowledge. In fact, there is no consolidated, recent overview of the state-of-the-art on software-change intentions (SCIs) and their relevance for software engineering. We present a meta-study of 122 publications, which we used to derive a categorization of SCIs; and to discuss motivations, evidence, and techniques relating to SCIs. Unfortunately, we found that individual pieces of research are often disconnected from each other because a common understanding is missing. Similarly, some publications showcase the potential of knowing SCIs, but more substantial research to understand the practical benefits of knowing SCIs is needed. Our contributions can help researchers and practitioners improve their understanding of SCIs and how SCIs can aid software engineering tasks.

Video coding that pursues the highest compression efficiency is the art of computing for rate-distortion optimization. The optimization has been approached in different ways, exemplified by two typical frameworks: block-based hybrid video coding and end-to-end learned video coding. The block-based hybrid framework encompasses more and more coding modes that are available at the decoder side; an encoder tries to search for the optimal coding mode for each block to be coded. This is an online, discrete, search-based optimization strategy. The end-to-end learned framework embraces more and more sophisticated neural networks; the network parameters are learned from a collection of videos, typically using gradient descent-based methods. This is an offline, continuous, numerical optimization strategy. Having analyzed these two strategies, both conceptually and with concrete schemes, this paper suggests investigating hybrid-optimization video coding, that is to combine online and offline, discrete and continuous, search-based and numerical optimization. For instance, we propose a hybrid-optimization video coding scheme, where the decoder consists of trained neural networks and supports several coding modes, and the encoder adopts both numerical and search-based algorithms for the online optimization. Our scheme achieves promising compression efficiency on par with H.265/HM for the random-access configuration.

Intelligent Transportation Systems (ITS) promise significant increases in throughput and reductions in trip delay. ITS makes extensive use of Connected and Autonomous Vehicles (CAV) frequently broadcasting location, speed, and intention information. However, with such extensive communication comes the risk to privacy. Preserving privacy while still exchanging vehicle state information has been recognized as an important problem.

Mix zones have emerged as a potentially effective way of protecting user privacy in ITS. CAVs are assigned pseudonyms to mask their identity; a mix zone is an area where CAVs can change their pseudonyms to resist being tracked.

In order to be effective, mix zone placement must take account of traffic flows. Also, since a mix zone can degrade throughput, mix zones must be used sparingly. Determining the number and placement of mix zones is a difficult dynamic optimization problem. This paper outlines the various approaches recently taken by researchers to deal with this problem.

Information sharing is vital in any communication network environment to enable network operating services take decisions based on the information collected by several deployed computing devices. The various networks that compose cyberspace, as Internet-of-Things (IoT) ecosystems, have significantly increased the need to constantly share information, which is often subject to disturbances. In this sense, the damage of anomalous operations boosted researches aimed at improving resilience to information sharing. Hence, in this survey, we present a systematization of knowledge about scientific efforts for achieving resilience to information sharing on networks. First, we introduce a taxonomy to organize the strategies applied to attain resilience to information sharing on networks, offering brief concepts about network anomalies and connectivity services. Then, we detail the taxonomy in the face of malicious threats, network disruptions, and performance issues, discussing the presented solutions. Next, we analyze the techniques existing in the literature to foster resilience to information exchanged on communication networks to verify their benefits and constraints. Throughout the text, we highlight and argue issues that restrain the use of these techniques during the design and runtime.

Intrusion Detection Systems (IDSs) are an essential element of modern cyber defense, alerting users to when and where cyber-attacks occur. Machine learning can enable IDSs to further distinguish between benign and malicious behaviors, but it comes with several challenges, including lack of quality training data and high false positive rates. Generative Machine Learning Models (GMLMs) can help overcome these challenges. This paper offers an in-depth exploration of GMLMs’ application to intrusion detection. It gives: (1) a systematic mapping study of research at the intersection of GMLMs and IDSs, and (2) a detailed review providing insights and directions for future research.

Voice is a natural mode of expression offered by modern computer-based systems. Qualitative perspectives on voice-based user experiences (voice UX) offer rich descriptions of complex interactions that numbers alone cannot fully represent. We conducted a systematic review of the literature on qualitative approaches to voice UX, capturing the nature of this body of work in a systematic map and offering a qualitative synthesis of findings. We highlight the benefits of qualitative methods for voice UX research, identify opportunities for increasing rigour in methods and outcomes, and distill patterns of experience across a diversity of devices and modes of qualitative praxis.

The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effective approach, federated learning (FL) is a natural solution for massive user-owned devices in edge computing with distributed and private training data. FL methods based on FedAvg typically follow a naive star topology, ignoring the heterogeneity and hierarchy of the volatile edge computing architectures and topologies in reality. Several other network topologies exist and can address the limitations and bottlenecks of the star topology. This motivates us to survey network topology-related FL solutions. In this paper, we conduct a comprehensive survey of the existing FL works focusing on network topologies. After a brief overview of FL and edge computing networks, we discuss various edge network topologies as well as their advantages and disadvantages. Lastly, we discuss the remaining challenges and future works for applying FL to topology-specific edge networks.

E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of job seekers for positions and on job seekers’ and recruiters’ preferences. Therefore, e-recruitment recommendation systems may greatly impact people’s careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the competitive edge of companies. Hence, it seems prudent to consider what (unique) challenges there are for recommendation systems in e-recruitment. Existing surveys on this topic discuss past studies from the algorithmic perspective, e.g., by categorizing them into collaborative filtering, content-based, and hybrid methods. This survey, instead, takes a complementary, challenge-based approach. We believe this is more practical for developers facing a concrete e-recruitment design task with a specific set of challenges, and also for researchers that look for impactful research projects in this domain. In this survey, we first identify the main challenges in the e-recruitment recommendation research. Next, we discuss how those challenges have been studied in the literature. Finally, we provide future research directions that we consider most promising in the e-recruitment recommendation domain.

The well-established deceptive design literature has focused on conventional user interfaces. With the rise of extended reality (XR), understanding deceptive design’s unique manifestations in this immersive domain is crucial. However, existing research lacks a full, cross-disciplinary analysis that analyzes how XR technologies enable new forms of deceptive design. Our study reviews the literature on deceptive design in XR environments. We use thematic synthesis to identify key themes. We found that XR’s immersive capabilities and extensive data collection enable subtle and powerful manipulation strategies. We identified eight themes outlining these strategies and discussed existing countermeasures. Our findings show the unique risks of deceptive design in XR, highlighting implications for researchers, designers, and policymakers. We propose future research directions that explore unintentional deceptive design, data-driven manipulation solutions, user education, and the link between ethical design and policy regulations.

The metaverse is an Artificial Intelligence (AI)-generated virtual world, in which people can game, work, learn and socialize. The realization of metaverse not only requires a large amount of computing resources to realize the rendering of the virtual world, but also requires communication resources to realize real-time transmission of massive data to ensure a good user experience. The metaverse is currently moving from fiction to reality with the development of advanced technologies represented by AI, blockchain, extended reality and Digital Twins (DT). However, due to the shortage of communication as well as computing resources, how to realize secure and efficient data interaction between the virtual and the real is an important issue for the metaverse. In this article, we first discuss the characteristics and architecture of the metaverse, and introduce its enabling technologies. To cope with the conflict between limited resources and user demands, the article next introduces an Integrated Sensing, Communication, and Computing (SCC) technology, and describes its basic principles and related characteristics of SCC. After that, solutions based on SCC in the metaverse scenarios are summarized and relevant lessons are summarized. Finally, we discuss some research challenges and open issues.