The Role of Generative AI in the Software Development Life Cycle: A Systematic Literature Review

Sebastian Saut Marulitua Sinaga; Zulfahmi Indra; Muhammad Rafli Wijaya; M Gali Almahdi

doi:10.59934/jaiea.v5i3.2431

Authors

Sebastian Saut Marulitua Sinaga Universitas Negeri Medan
Zulfahmi Indra Universitas Negeri Medan
Muhammad Rafli Wijaya Universitas Negeri Medan
M Gali Almahdi Universitas Negeri Medan

DOI:

https://doi.org/10.59934/jaiea.v5i3.2431

Keywords:

Network Development Life Cycle, Large Language Models, Generative AI, SLR

Abstract

The rapid advancement of Generative Artificial Intelligence (Generative AI), particularly through the emergence of Large Language Models (LLMs), has significantly transformed modern software engineering practices. These technologies enable automation across various phases of the Software Development Life Cycle (SDLC), including system design, coding, testing, and software maintenance. Despite their potential to improve development efficiency, the widespread adoption of Generative AI also introduces critical concerns related to software security, code quality, and long-term maintainability. This study aims to analyze the opportunities, security risks, and mitigation strategies associated with the integration of Generative AI into the SDLC. A Systematic Literature Review (SLR) with a qualitative descriptive approach was conducted by examining 15 primary studies published between 2021 and 2026, retrieved from IEEE Xplore, ACM Digital Library, Scopus, Google Scholar, and Portal Garuda. The collected literature was analyzed using content analysis and thematic analysis to identify the impacts of Generative AI across different SDLC phases. The findings reveal that Generative AI significantly enhances developer productivity, achieving efficiency gains of approximately 35% during system design, 55% during coding, 45% during testing, and 40% during software maintenance. However, AI-generated code remains vulnerable to various security weaknesses, including SQL Injection, Cross-Site Scripting (XSS), and improper input validation. Furthermore, excessive reliance on AI-generated outputs may contribute to technical debt accumulation through code duplication and reduced refactoring activities. To address these challenges, this study recommends the implementation of a Stratified AI-Human Governance Framework (SAHGF), which combines automated security validation, human code review, security testing, and continuous monitoring.

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