6G Software Engineering: A Systematic Mapping Study
- URL: http://arxiv.org/abs/2405.05017v1
- Date: Wed, 8 May 2024 12:34:33 GMT
- Title: 6G Software Engineering: A Systematic Mapping Study
- Authors: Ruoyu Su, Xiaozhou Li, Davide Taibi,
- Abstract summary: 6G will revolutionize the software world allowing faster cellular communications and a massive number of connected devices.
Current cloud solutions, where all the data is transferred and computed in the cloud, are not sustainable in such a large network of devices.
We conduct a Systematic Mapping Study to investigate the current research status of 6G Software Engineering.
- Score: 4.2954732881492514
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 6G will revolutionize the software world allowing faster cellular communications and a massive number of connected devices. 6G will enable a shift towards a continuous edge-to-cloud architecture. Current cloud solutions, where all the data is transferred and computed in the cloud, are not sustainable in such a large network of devices. Current technologies, including development methods, software architectures, and orchestration and offloading systems, still need to be prepared to cope with such requirements. In this paper, we conduct a Systematic Mapping Study to investigate the current research status of 6G Software Engineering. Results show that 18 research papers have been proposed in software process, software architecture, orchestration and offloading methods. Of these, software architecture and software-defined networks are respectively areas and topics that have received the most attention in 6G Software Engineering. In addition, the main types of results of these papers are methods, architectures, platforms, frameworks and algorithms. For the five tools/frameworks proposed, they are new and not currently studied by other researchers. The authors of these findings are mainly from China, India and Saudi Arabia. The results will enable researchers and practitioners to further research and extend for 6G Software Engineering.
Related papers
- Deep Learning-based Software Engineering: Progress, Challenges, and Opportunities [29.934835831037347]
We present the first task-oriented survey on deep learning-based software engineering.
It covers twelve major software engineering subareas significantly impacted by deep learning techniques.
arXiv Detail & Related papers (2024-10-17T00:46:00Z) - Morescient GAI for Software Engineering [2.4861619769660637]
Using Generative AI (GAI) for software engineering tasks is one of the most rapidly expanding fields of software engineering research.
We present a vision for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.
arXiv Detail & Related papers (2024-06-07T07:38:33Z) - Requirements Engineering for Research Software: A Vision [2.2217676348694213]
Most researchers creating software for scientific purposes are not trained in Software Engineering.
Research software is often developed ad hoc without following stringent processes.
We describe how researchers elicit, document, and analyze requirements for research software.
arXiv Detail & Related papers (2024-05-13T14:25:01Z) - Generative Artificial Intelligence for Software Engineering -- A
Research Agenda [8.685607624226037]
We conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for Software Engineering.
Our results show that it is possible to explore the adoption of GenAI in partial automation and support decision-making in all software development activities.
Common considerations when implementing GenAI include industry-level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology.
arXiv Detail & Related papers (2023-10-28T09:14:39Z) - Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End
Collaboration [56.330705072736166]
We propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, and outline a novel cloud-edge-end collaboration paradigm.
As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system.
arXiv Detail & Related papers (2023-10-26T15:19:40Z) - In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile
Networks [61.416494781759326]
In-situ model downloading aims to achieve transparent and real-time replacement of on-device AI models by downloading from an AI library in the network.
A key component of the presented framework is a set of techniques that dynamically compress a downloaded model at the depth-level, parameter-level, or bit-level.
We propose a 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library.
arXiv Detail & Related papers (2022-10-07T13:41:15Z) - A modular software framework for the design and implementation of
ptychography algorithms [55.41644538483948]
We present SciCom, a new ptychography software framework aiming at simulating ptychography datasets and testing state-of-the-art reconstruction algorithms.
Despite its simplicity, the software leverages accelerated processing through the PyTorch interface.
Results are shown on both synthetic and real datasets.
arXiv Detail & Related papers (2022-05-06T16:32:37Z) - Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing.
In this article, we identify the key requirements and challenges of edge-native AI in 6G.
arXiv Detail & Related papers (2020-10-01T02:16:40Z) - A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G:
Integrating Domain Knowledge into Deep Learning [115.75967665222635]
Ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.
Deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks.
This tutorial illustrates how domain knowledge can be integrated into different kinds of deep learning algorithms for URLLC.
arXiv Detail & Related papers (2020-09-13T14:53:01Z) - Swarm Intelligence for Next-Generation Wireless Networks: Recent
Advances and Applications [39.38804488121544]
Swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks.
We provide an overview of SI techniques from fundamental concepts to well-knowns.
We review the applications of SI to settle emerging issues in next-generation wireless networks.
arXiv Detail & Related papers (2020-07-30T04:32:49Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.