智能、可扩展和性能优化软件开发的综合框架

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access Pub Date : 2025-04-24 DOI:10.1109/ACCESS.2025.3564139

Noor Arshad;Talal Ashraf Butt;Muhammad Iqbal

{"title":"智能、可扩展和性能优化软件开发的综合框架","authors":"Noor Arshad;Talal Ashraf Butt;Muhammad Iqbal","doi":"10.1109/ACCESS.2025.3564139","DOIUrl":null,"url":null,"abstract":"Integrating Artificial Intelligence (AI) into the Software Development Life Cycle (SDLC) has become necessary to enhance efficiency, scalability, and performance in modern software systems. Instead of incorporating the AI functionality into their SDLC, traditional SDLC models typically add-on the AI software functionality after they have integrated AI functionality into their application or software process. Because of this, developers undergo inefficiencies in their development workflows, experience performance bottlenecks during testing, and experience challenges of incorporating AI to improve an application’s performance through optimization. This paper proposes a new AI-Optimized Software Development Life Cycle (AI-SDLC), which is a holistic and comprehensive framework that encases the embedded AI capabilities and optimization strategies throughout the SDLC process during every stage of the system development, so that requirements-gathering, development, testing, and maintenance are hybrid software processes and not dictated by AI vs. traditional software development processes. AI-SDLC presents new development roles, such as AI Integration Specialist, Code Optimizer, and UX Optimization Specialist, which helps developers work across disciplines and increases collaborative interaction between traditional developers and AI engineers. AI-SDLC also utilizes an AI-driven automated hybrid software process in areas such as requirement elicitation, design/architecture validation, testing, deployment monitoring, and scalability to produce robust high-performance systems in all areas of practicing software development life cycle work. The discourse includes a rich case study based on a Smart Logistics Management System to demonstrate practical implementation of the AI-SDLC and how it facilitates improvement in system efficiency and improved user experience. Additionally, the discussion also highlights the possibilities of AI-SDLC practical implementation in other industrial domain areas such as e-Commerce, finance, aviation and enterprise solution based projects with practical considerations for implementation. In conclusion, the discussion provides findings that support AI-SDLC as a structured and intelligence-driven approach to Software Development Life Cycle implementation that addresses the weaknesses of traditional software design and development frameworks.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"74062-74077"},"PeriodicalIF":3.4000,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10975747","citationCount":"0","resultStr":"{\"title\":\"A Comprehensive Framework for Intelligent, Scalable, and Performance-Optimized Software Development\",\"authors\":\"Noor Arshad;Talal Ashraf Butt;Muhammad Iqbal\",\"doi\":\"10.1109/ACCESS.2025.3564139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Integrating Artificial Intelligence (AI) into the Software Development Life Cycle (SDLC) has become necessary to enhance efficiency, scalability, and performance in modern software systems. Instead of incorporating the AI functionality into their SDLC, traditional SDLC models typically add-on the AI software functionality after they have integrated AI functionality into their application or software process. Because of this, developers undergo inefficiencies in their development workflows, experience performance bottlenecks during testing, and experience challenges of incorporating AI to improve an application’s performance through optimization. This paper proposes a new AI-Optimized Software Development Life Cycle (AI-SDLC), which is a holistic and comprehensive framework that encases the embedded AI capabilities and optimization strategies throughout the SDLC process during every stage of the system development, so that requirements-gathering, development, testing, and maintenance are hybrid software processes and not dictated by AI vs. traditional software development processes. AI-SDLC presents new development roles, such as AI Integration Specialist, Code Optimizer, and UX Optimization Specialist, which helps developers work across disciplines and increases collaborative interaction between traditional developers and AI engineers. AI-SDLC also utilizes an AI-driven automated hybrid software process in areas such as requirement elicitation, design/architecture validation, testing, deployment monitoring, and scalability to produce robust high-performance systems in all areas of practicing software development life cycle work. The discourse includes a rich case study based on a Smart Logistics Management System to demonstrate practical implementation of the AI-SDLC and how it facilitates improvement in system efficiency and improved user experience. Additionally, the discussion also highlights the possibilities of AI-SDLC practical implementation in other industrial domain areas such as e-Commerce, finance, aviation and enterprise solution based projects with practical considerations for implementation. In conclusion, the discussion provides findings that support AI-SDLC as a structured and intelligence-driven approach to Software Development Life Cycle implementation that addresses the weaknesses of traditional software design and development frameworks.\",\"PeriodicalId\":13079,\"journal\":{\"name\":\"IEEE Access\",\"volume\":\"13 \",\"pages\":\"74062-74077\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10975747\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Access\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10975747/\",\"RegionNum\":3,\"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":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10975747/","RegionNum":3,"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}

引用次数: 0

摘要

将人工智能（AI）集成到软件开发生命周期（SDLC）中已经成为提高现代软件系统效率、可扩展性和性能的必要条件。传统的SDLC模型通常是在将AI功能集成到应用程序或软件过程之后，再添加AI软件功能，而不是将AI功能合并到SDLC中。正因为如此，开发人员在他们的开发工作流程中经历了效率低下，在测试过程中经历了性能瓶颈，并且经历了整合AI以通过优化来改进应用程序性能的挑战。本文提出了一个新的人工智能优化软件开发生命周期（AI-SDLC），它是一个整体和全面的框架，将嵌入式人工智能功能和优化策略封装在系统开发的每个阶段的整个SDLC过程中，以便需求收集，开发，测试和维护是混合软件过程，而不是由人工智能与传统软件开发过程决定。AI- sdlc提供了新的开发角色，如AI集成专家，代码优化器和UX优化专家，这有助于开发人员跨学科工作，并增加传统开发人员和AI工程师之间的协作互动。AI-SDLC还在需求引出、设计/架构验证、测试、部署监控和可伸缩性等领域利用ai驱动的自动化混合软件过程，在实践软件开发生命周期工作的所有领域产生健壮的高性能系统。该演讲包括一个基于智能物流管理系统的丰富案例研究，以展示AI-SDLC的实际实施，以及它如何促进系统效率的提高和用户体验的改善。此外，讨论还强调了AI-SDLC在其他工业领域如电子商务、金融、航空和基于企业解决方案的项目中实际实施的可能性，并提出了实施的实际考虑。总之，讨论提供了支持AI-SDLC作为结构化和智能驱动的软件开发生命周期实现方法的发现，该方法解决了传统软件设计和开发框架的弱点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A Comprehensive Framework for Intelligent, Scalable, and Performance-Optimized Software Development

Integrating Artificial Intelligence (AI) into the Software Development Life Cycle (SDLC) has become necessary to enhance efficiency, scalability, and performance in modern software systems. Instead of incorporating the AI functionality into their SDLC, traditional SDLC models typically add-on the AI software functionality after they have integrated AI functionality into their application or software process. Because of this, developers undergo inefficiencies in their development workflows, experience performance bottlenecks during testing, and experience challenges of incorporating AI to improve an application’s performance through optimization. This paper proposes a new AI-Optimized Software Development Life Cycle (AI-SDLC), which is a holistic and comprehensive framework that encases the embedded AI capabilities and optimization strategies throughout the SDLC process during every stage of the system development, so that requirements-gathering, development, testing, and maintenance are hybrid software processes and not dictated by AI vs. traditional software development processes. AI-SDLC presents new development roles, such as AI Integration Specialist, Code Optimizer, and UX Optimization Specialist, which helps developers work across disciplines and increases collaborative interaction between traditional developers and AI engineers. AI-SDLC also utilizes an AI-driven automated hybrid software process in areas such as requirement elicitation, design/architecture validation, testing, deployment monitoring, and scalability to produce robust high-performance systems in all areas of practicing software development life cycle work. The discourse includes a rich case study based on a Smart Logistics Management System to demonstrate practical implementation of the AI-SDLC and how it facilitates improvement in system efficiency and improved user experience. Additionally, the discussion also highlights the possibilities of AI-SDLC practical implementation in other industrial domain areas such as e-Commerce, finance, aviation and enterprise solution based projects with practical considerations for implementation. In conclusion, the discussion provides findings that support AI-SDLC as a structured and intelligence-driven approach to Software Development Life Cycle implementation that addresses the weaknesses of traditional software design and development frameworks.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

期刊介绍： IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.