[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120747-en":3,"doc-seo-120747-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120747,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",6,"Technology","Framework for the Automation of SDLC Phases using Artificial Intelligence and Machine Learning Techniques","Software Engineering provides a common roadmap for building software, and failure to follow well-defined SDLC models historically leads to project breakdowns. Agile SDLC is widely used, yet recent work increasingly seeks automation across Requirements Analysis, Design, Coding, Testing, and Operations & Maintenance. This paper proposes a framework to apply Artificial Intelligence and Machine Learning techniques in each SDLC phase, aiming to improve execution quality, reduce costs and time, and minimize manual effort while increasing overall efficiency and effectiveness.","Framework for the Automation ofSDLC Phases using Artificial Intelligence and Machine Learning  \nTechniques  \nSahana P. Shankar1, Shilpa Shashikant Chaudhari2  \n1Department of Computer Science and Engineering  \nRamaiah University of Applied Sciences  \nBengaluru, India  \ne-mail: [sahanaprabhushankar@gmail.com](sahanaprabhushankar@gmail.com)  \n2Department of Computer Science and Engineering  \nM S Ramaiah Institute of Technology (Affiliated to VTU)  \nBengaluru, India  \ne-mail: [shilpasc29@msrit.edu](shilpasc29@msrit.edu)  \nAbstract— Software Engineering acts as a foundation stone for any software that is being built. It provides a common road-map for construction of software from any domain. Not following a well-defined Software Development Model have led to the failure of many software projects in the past. Agile is the Software Development Life Cycle (SDLC) Model that is widely used in practice in the IT industries to develop software on various technologies such as Big Data, Machine Learning, Artificial Intelligence, Deep learning. The focus on Software Engineering side in the recent years has been on trying to automate the various phases of SDLC namely-Requirements Analysis, Design, Coding, Testing and Operations and Maintenance. Incorporating latest trending technologies such as Machine Learning and Artificial Intelligence into various phases of SDLC, could facilitate for better execution of each of these phases. This in turn helps to cut-down costs, save time, improve the efficiency and reduce the manual effort required for each of these phases. The aim of this paper is to present a framework for the application of various Artificial Intelligence and Machine Learning techniques in the different phases of SDLC.  \nKeywords-Requirements Elicitation, Knowledge Bank, Testing, Software Maintenance, Chatbot, Design.  \nI. INTRODUCTION  \nSoftware Engineering (SE) can be defined as application of engineering skills to the development of a software product. Over the years engineers have spent much time in building more intelligent software. With the increase in the level of intelligence associated with the software, the level of complexity also seems to be increasing. Complex software again poses new challenges to the software engineers at each of the phases of the software development life cycle (SDLC) . Where developing complex system is a challenging task to the software engineers, developing intelligent ways of building the complex intelligent systems can be considered to a bigger challenge in itself. If a software engineer is able to achieve the latter task, it could as well simplify and aid in improving the efficiency of the earlier task. The Figure 1 below shows the relationship between  \nPlanning, Decision and Searching in terms of AI, ML and SE where SE involves more of planning, AI involves searching and ML involves decision making.  \nFig 1. Connectivity between AI-ML-SE  \nArtificial Intelligence has gained a lot of popularity in the recent years in the various fields of Automotive, Banking, Medicine, Retails and Service Industry. It has been observed that AI has made its way into mundane everyday tasks of people in day today life. With rapid growth and extensive research work being carried out in the field of AI and ML at an exponential rate, these results can be used to improve the Software Engineering Process.  \nThe different phases of SDLC on a high level can be broadly classified as Requirement Analysis, Design, Implementation, Testing, Operation and Maintenance. These phases will be present in any Software development model in additional to a few more phases. The Requirements phase involves mainly elicitation of the business requirements from the clients or stakeholders through personal interviews or brainstorming. These requirements that are collected in Natural Language such as English are then converted to a more formal representation of data for the Software Requirements Specification Document. The conversion from I","cbCaigg6ebuh7G6t","https://ap.wps.com/l/cbCaigg6ebuh7G6t","pdf",342565,1,12,"English","en",105,"# Introduction\n## AI–ML–SE connectivity\n## SDLC phases and typical artifacts\n# Requirements Analysis\n## Natural language to SRS and risk documents\n## Selecting implementable requirements\n# Design Phase\n## Architectural design and design patterns\n## Metrics-driven decisions\n# Implementation and Coding\n## Product metrics and developer output\n## White-box testing choices\n# Testing Phase\n## Black-box testing, test cases, and test plan\n## Regression testing and automation tools\n# Operations and Maintenance","[{\"question\":\"Why is automating SDLC phases important?\",\"answer\":\"A well-defined SDLC model reduces the risk of software project failure. Automating Requirements, Design, Coding, Testing, and Operations \\u0026 Maintenance can cut costs, save time, and reduce manual effort while improving efficiency.\"},{\"question\":\"What does the framework focus on regarding AI and ML?\",\"answer\":\"The framework targets using Artificial Intelligence and Machine Learning techniques in different SDLC phases to enhance execution and outcomes across the software engineering workflow.\"},{\"question\":\"How are requirements handled before design in the described process?\",\"answer\":\"Requirements elicited from stakeholders in natural language are converted into formal representations for the Software Requirements Specification (SRS) document, supported by risk analysis and mitigation and decisions on which requirements to implement.\"}]","Framework for the Automation of SDLC Phases using Artificial Intelligence and Machine Learning Techniques | PDF",1785731818,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"framework-for-the-automation-of-sdlc-phases-using-artificial-intelligence-and-machine-learning-techniques","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/framework-for-the-automation-of-sdlc-phases-using-artificial-intelligence-and-machine-learning-techniques/120747/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is automating SDLC phases important?","Question",{"text":75,"@type":76},"A well-defined SDLC model reduces the risk of software project failure. Automating Requirements, Design, Coding, Testing, and Operations & Maintenance can cut costs, save time, and reduce manual effort while improving efficiency.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the framework focus on regarding AI and ML?",{"text":80,"@type":76},"The framework targets using Artificial Intelligence and Machine Learning techniques in different SDLC phases to enhance execution and outcomes across the software engineering workflow.",{"name":82,"@type":73,"acceptedAnswer":83},"How are requirements handled before design in the described process?",{"text":84,"@type":76},"Requirements elicited from stakeholders in natural language are converted into formal representations for the Software Requirements Specification (SRS) document, supported by risk analysis and mitigation and decisions on which requirements to implement.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":29,"slug":121},8,"Research & Report","research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]