Clara Ayora, Arturo S. García, Jose Luis de la Vara
{"title":"边缘人工智能保证:系统的映射研究","authors":"Clara Ayora, Arturo S. García, Jose Luis de la Vara","doi":"10.1016/j.infsof.2025.107908","DOIUrl":null,"url":null,"abstract":"<div><h3>Context</h3><div>In critical domains, assurance corresponds to the set of activities to provide confidence that a system can be deemed dependable, e.g., safe and secure. This essential system and software engineering process is usually conducted according to standards. For novel applications running at the edge and using artificial intelligence (AI), how to conduct assurance in a systematic way is still under study.</div></div><div><h3>Objective</h3><div>The goal of this paper is to provide a comprehensive understanding of current Edge AI assurance considerations. Our interest lies in contributing insights that offer a forward-looking perspective on what is essential in this research field.</div></div><div><h3>Method</h3><div>We conducted a systematic mapping study (SMS) to characterize how Edge AI assurance is addressed in existing literature. The SMS was built on 38 studies, selected through a multi-stage process, from 3113 studies published between 2019 and 2025. The 38 studies were deeply analysed focusing on seven research questions about the main key Edge AI assurance aspects: dependability concerns, application domains, assurance standards, assurance evidence, dependability justification techniques, and edge and AI characteristics.</div></div><div><h3>Results</h3><div>We found ten dependability concerns that have been addressed (e.g., safety and security), six application domains (e.g., Industry 4.0), eight assurance standards and regulations (e.g., ISO 26262), 27 types of assurance evidence (e.g., architecture specification), three dependability justification techniques (e.g., argumentation), five AI-specific characteristics (e.g., machine learning algorithms) and five edge-specific characteristics (e.g., network).</div></div><div><h3>Conclusions</h3><div>The paper is, to our knowledge, the only existing review on the topic of Edge AI assurance. The results are relevant to practitioners seeking a better grasp on this field as well as researchers to find new research gaps. We have also identified research areas where more effort can be undertaken (e.g., multi-concern assurance).</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"189 ","pages":"Article 107908"},"PeriodicalIF":4.3000,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Edge AI assurance: A systematic mapping study\",\"authors\":\"Clara Ayora, Arturo S. García, Jose Luis de la Vara\",\"doi\":\"10.1016/j.infsof.2025.107908\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Context</h3><div>In critical domains, assurance corresponds to the set of activities to provide confidence that a system can be deemed dependable, e.g., safe and secure. This essential system and software engineering process is usually conducted according to standards. For novel applications running at the edge and using artificial intelligence (AI), how to conduct assurance in a systematic way is still under study.</div></div><div><h3>Objective</h3><div>The goal of this paper is to provide a comprehensive understanding of current Edge AI assurance considerations. Our interest lies in contributing insights that offer a forward-looking perspective on what is essential in this research field.</div></div><div><h3>Method</h3><div>We conducted a systematic mapping study (SMS) to characterize how Edge AI assurance is addressed in existing literature. The SMS was built on 38 studies, selected through a multi-stage process, from 3113 studies published between 2019 and 2025. The 38 studies were deeply analysed focusing on seven research questions about the main key Edge AI assurance aspects: dependability concerns, application domains, assurance standards, assurance evidence, dependability justification techniques, and edge and AI characteristics.</div></div><div><h3>Results</h3><div>We found ten dependability concerns that have been addressed (e.g., safety and security), six application domains (e.g., Industry 4.0), eight assurance standards and regulations (e.g., ISO 26262), 27 types of assurance evidence (e.g., architecture specification), three dependability justification techniques (e.g., argumentation), five AI-specific characteristics (e.g., machine learning algorithms) and five edge-specific characteristics (e.g., network).</div></div><div><h3>Conclusions</h3><div>The paper is, to our knowledge, the only existing review on the topic of Edge AI assurance. The results are relevant to practitioners seeking a better grasp on this field as well as researchers to find new research gaps. We have also identified research areas where more effort can be undertaken (e.g., multi-concern assurance).</div></div>\",\"PeriodicalId\":54983,\"journal\":{\"name\":\"Information and Software Technology\",\"volume\":\"189 \",\"pages\":\"Article 107908\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information and Software Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950584925002472\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information and Software Technology","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950584925002472","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
In critical domains, assurance corresponds to the set of activities to provide confidence that a system can be deemed dependable, e.g., safe and secure. This essential system and software engineering process is usually conducted according to standards. For novel applications running at the edge and using artificial intelligence (AI), how to conduct assurance in a systematic way is still under study.
Objective
The goal of this paper is to provide a comprehensive understanding of current Edge AI assurance considerations. Our interest lies in contributing insights that offer a forward-looking perspective on what is essential in this research field.
Method
We conducted a systematic mapping study (SMS) to characterize how Edge AI assurance is addressed in existing literature. The SMS was built on 38 studies, selected through a multi-stage process, from 3113 studies published between 2019 and 2025. The 38 studies were deeply analysed focusing on seven research questions about the main key Edge AI assurance aspects: dependability concerns, application domains, assurance standards, assurance evidence, dependability justification techniques, and edge and AI characteristics.
Results
We found ten dependability concerns that have been addressed (e.g., safety and security), six application domains (e.g., Industry 4.0), eight assurance standards and regulations (e.g., ISO 26262), 27 types of assurance evidence (e.g., architecture specification), three dependability justification techniques (e.g., argumentation), five AI-specific characteristics (e.g., machine learning algorithms) and five edge-specific characteristics (e.g., network).
Conclusions
The paper is, to our knowledge, the only existing review on the topic of Edge AI assurance. The results are relevant to practitioners seeking a better grasp on this field as well as researchers to find new research gaps. We have also identified research areas where more effort can be undertaken (e.g., multi-concern assurance).
期刊介绍:
Information and Software Technology is the international archival journal focusing on research and experience that contributes to the improvement of software development practices. The journal''s scope includes methods and techniques to better engineer software and manage its development. Articles submitted for review should have a clear component of software engineering or address ways to improve the engineering and management of software development. Areas covered by the journal include:
• Software management, quality and metrics,
• Software processes,
• Software architecture, modelling, specification, design and programming
• Functional and non-functional software requirements
• Software testing and verification & validation
• Empirical studies of all aspects of engineering and managing software development
Short Communications is a new section dedicated to short papers addressing new ideas, controversial opinions, "Negative" results and much more. Read the Guide for authors for more information.
The journal encourages and welcomes submissions of systematic literature studies (reviews and maps) within the scope of the journal. Information and Software Technology is the premiere outlet for systematic literature studies in software engineering.