The Impact of AI on Software Development Lifecycle : A Case Study on Wolfbyte
Quyen, Nghi (2024)
2024
Tieto- ja sähkötekniikan kandidaattiohjelma - Bachelor's Programme in Computing and Electrical Engineering
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2024-05-13
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202404305103
https://urn.fi/URN:NBN:fi:tuni-202404305103
Tiivistelmä
In an era where Artificial Intelligence (AI) has been rapidly growing across various fields, its integration within software development promises a new frontier of productivity and performance. AI used in software engineering is multifaceted, including automated test-ing, enhanced debugging, code completion, and project management. Notable tools such as Chat GPT and GitHub Copilot have emerged as useful assistants throughout the development lifecycle and promise further advancement with their evolution.
The Software Development Life Cycle (SDLC), particularly in its Agile iteration, serves as the backbone for systematic software creation. Agile methodologies highlight flexibil-ity, continuous delivery, and adaptive planning, making them suitable for integration with AI. This study focuses on AI's role within the three initial stages of ASDLC— Planning and Requirements gathering, Design, and Development.
The main methodology is a Qualitative Descriptive Case Study with the investigation fo-cused on the Wolfbyte project— an example of Agile practice integrated with AI tools. This qualitative approach followed a structured methodology to explore AI's contribu-tions and challenges within software development. The Wolfbyte case study demon-strated that AI could substantially streamline processes across the early stages of ASDLC. During the planning and requirements gathering phase, AI significantly helped in drafting project documentation and requirements specifications. In the design phase, AI facilitated the creation of architectural solutions. In the development phase, the study focuses on AI’s role in code generation and code quality assessment through the So-narQube tool.
The research acknowledged limitations, primarily the absence of pre-AI integration baselines, which restricts the precise measurement of AI's influence. The recommenda-tions include applying AI to later stages of ASDLC and gathering more comprehensive data to improve future research.
In conclusion, the study provides evidence of AI's positive impact within ASDLC's initial stages, as demonstrated by the Wolfbyte project. The insights underscore AI’s potential to revolutionize Agile software development practices.
The Software Development Life Cycle (SDLC), particularly in its Agile iteration, serves as the backbone for systematic software creation. Agile methodologies highlight flexibil-ity, continuous delivery, and adaptive planning, making them suitable for integration with AI. This study focuses on AI's role within the three initial stages of ASDLC— Planning and Requirements gathering, Design, and Development.
The main methodology is a Qualitative Descriptive Case Study with the investigation fo-cused on the Wolfbyte project— an example of Agile practice integrated with AI tools. This qualitative approach followed a structured methodology to explore AI's contribu-tions and challenges within software development. The Wolfbyte case study demon-strated that AI could substantially streamline processes across the early stages of ASDLC. During the planning and requirements gathering phase, AI significantly helped in drafting project documentation and requirements specifications. In the design phase, AI facilitated the creation of architectural solutions. In the development phase, the study focuses on AI’s role in code generation and code quality assessment through the So-narQube tool.
The research acknowledged limitations, primarily the absence of pre-AI integration baselines, which restricts the precise measurement of AI's influence. The recommenda-tions include applying AI to later stages of ASDLC and gathering more comprehensive data to improve future research.
In conclusion, the study provides evidence of AI's positive impact within ASDLC's initial stages, as demonstrated by the Wolfbyte project. The insights underscore AI’s potential to revolutionize Agile software development practices.