AI-Analyst: An AI-Assisted SDLC Analysis Framework for Business Cost Optimization

Managing the System Development Lifecycle (SDLC) is a complex task because of its involvement in coordinating diverse activities, stakeholders, and resources while ensuring project goals are met efficiently. The complex nature of the SDLC process leaves plenty of scope for human error, which impacts...

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Main Authors: Nuruzzaman Faruqui, Priyabrata Thatoi, Rohit Choudhary, Ivana Roncevic, Hamed Alqahtani, Iqbal H. Sarker, Shapla Khanam
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/10804767/
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author Nuruzzaman Faruqui
Priyabrata Thatoi
Rohit Choudhary
Ivana Roncevic
Hamed Alqahtani
Iqbal H. Sarker
Shapla Khanam
author_facet Nuruzzaman Faruqui
Priyabrata Thatoi
Rohit Choudhary
Ivana Roncevic
Hamed Alqahtani
Iqbal H. Sarker
Shapla Khanam
author_sort Nuruzzaman Faruqui
collection DOAJ
description Managing the System Development Lifecycle (SDLC) is a complex task because of its involvement in coordinating diverse activities, stakeholders, and resources while ensuring project goals are met efficiently. The complex nature of the SDLC process leaves plenty of scope for human error, which impacts the overall business cost. This paper introduces AI-Analyst, an AI-assisted framework developed using the transformer-based model with more than 150 million parameters to assist with SDLC management. It minimizes manual effort errors, optimizes resource allocation, and improves decision-making processes, resulting in substantial cost savings. The statistical analysis shows that it saves around 53.33% of costs in an experimental project. The transformer model has been trained with a uniquely prepared dataset tailored for SDLC through transfer learning. It achieved impressive results, with an accuracy of 91.5%, precision of 91.9%, recall of 91.3%, and an F1-score of 91.5%, demonstrating its high reliability and performance. The perplexity score of 15 further indicates the model’s strong language understanding capabilities to retrieve relations from complex characteristics of Natural Language Processing (NLP). The AI-Analyst framework represents a significant advancement in integrating Large Language Models (LLMs) into SDLC, offering a scalable and cost-effective solution for optimizing business processes.
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spelling doaj-art-84c1700c7a4640298f56ea0fdf883fb82025-08-20T02:01:30ZengIEEEIEEE Access2169-35362024-01-011219518819520310.1109/ACCESS.2024.351942310804767AI-Analyst: An AI-Assisted SDLC Analysis Framework for Business Cost OptimizationNuruzzaman Faruqui0https://orcid.org/0000-0001-9306-9637Priyabrata Thatoi1https://orcid.org/0009-0009-5994-2637Rohit Choudhary2Ivana Roncevic3https://orcid.org/0000-0002-9373-0023Hamed Alqahtani4https://orcid.org/0000-0002-6427-7768Iqbal H. Sarker5https://orcid.org/0000-0003-1740-5517Shapla Khanam6https://orcid.org/0000-0003-4012-086XDepartment of Software Engineering, Daffodil International University, Daffodil Smart City, Dhaka, BangladeshAmazon, Chicago, IL, USAAmazon, Dallas, TX, USADepartment of Linguistics and Translation, Applied Linguistics Research Laboratory, Prince Sultan University, Riyadh, Saudi ArabiaInformatics and Computer Systems Department, Center of Artificial Intelligence, College of Computer Science, King Khalid University, Abha, Saudi ArabiaCentre for Securing Digital Futures, School of Science, Edith Cowan University, Perth, WA, AustraliaFaculty of Computing and Digital Technology, HELP University, Kuala Lumpur, MalaysiaManaging the System Development Lifecycle (SDLC) is a complex task because of its involvement in coordinating diverse activities, stakeholders, and resources while ensuring project goals are met efficiently. The complex nature of the SDLC process leaves plenty of scope for human error, which impacts the overall business cost. This paper introduces AI-Analyst, an AI-assisted framework developed using the transformer-based model with more than 150 million parameters to assist with SDLC management. It minimizes manual effort errors, optimizes resource allocation, and improves decision-making processes, resulting in substantial cost savings. The statistical analysis shows that it saves around 53.33% of costs in an experimental project. The transformer model has been trained with a uniquely prepared dataset tailored for SDLC through transfer learning. It achieved impressive results, with an accuracy of 91.5%, precision of 91.9%, recall of 91.3%, and an F1-score of 91.5%, demonstrating its high reliability and performance. The perplexity score of 15 further indicates the model’s strong language understanding capabilities to retrieve relations from complex characteristics of Natural Language Processing (NLP). The AI-Analyst framework represents a significant advancement in integrating Large Language Models (LLMs) into SDLC, offering a scalable and cost-effective solution for optimizing business processes.https://ieeexplore.ieee.org/document/10804767/Transformer modellarge language modelsystem development lifecycletransfer learningartificial intelligencebusiness cost optimization
spellingShingle Nuruzzaman Faruqui
Priyabrata Thatoi
Rohit Choudhary
Ivana Roncevic
Hamed Alqahtani
Iqbal H. Sarker
Shapla Khanam
AI-Analyst: An AI-Assisted SDLC Analysis Framework for Business Cost Optimization
IEEE Access
Transformer model
large language model
system development lifecycle
transfer learning
artificial intelligence
business cost optimization
title AI-Analyst: An AI-Assisted SDLC Analysis Framework for Business Cost Optimization
title_full AI-Analyst: An AI-Assisted SDLC Analysis Framework for Business Cost Optimization
title_fullStr AI-Analyst: An AI-Assisted SDLC Analysis Framework for Business Cost Optimization
title_full_unstemmed AI-Analyst: An AI-Assisted SDLC Analysis Framework for Business Cost Optimization
title_short AI-Analyst: An AI-Assisted SDLC Analysis Framework for Business Cost Optimization
title_sort ai analyst an ai assisted sdlc analysis framework for business cost optimization
topic Transformer model
large language model
system development lifecycle
transfer learning
artificial intelligence
business cost optimization
url https://ieeexplore.ieee.org/document/10804767/
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