Technological Evolution In Software Development Life Cycle: The Implications Of Artificial Intelligence

Engineering notes

Engineering notes will be added by the aipentium editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

The Software Development Lifecycle (SDLC) is a pathway for software designing, modification, evaluation and optimisation. Baseline models like Waterfall and Agile go through problematic situations like inflexibility, delayed error detection, scope creep and obstacles in the management of complex projects. To tackle these anomalies, Artificial Intelligence (AI) took the spotlight by intensifying various phases through automation, predictive analytics, and smart decision-making. This review addresses the outcomes of AI in SDLC, outlining the improvement in required analysis, system designing, code generation and bugs detection as well as testing and deployment. AI technologies like GitHub Copilot and ChatGPT are reinforced to be the real-world applications that alter the developer productivity and software quality. AI serves various purposes like increased efficiency, reduced development times, higher accuracy and early bug detection. Despite these benefits, the study illustrates various demerits like data dependency and lack of transparency as well as ethical concerns. But it enhances the requirement of standardised frameworks, enriched explainability and optimised cooperation among humans and AI. Ultimately, AI can considerably boost the SDLC, but a balanced automation with human expertise is important to shape the future of software engineering by promoting more efficiency and steady and knowledgeable development in the processes.

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