Systematic Literature Review on Analyzing the Impact of Prompt Engineering on Efficiency, Code Quality, and Security in Crud Application Development

This research investigates the impact of prompt engineering on the efficiency, code quality, and security of CRUD (Create, Read, Update, Delete) operations in software development using large language models (LLMs) like ChatGPT. Prompt engineering, which involves crafting specific inputs to guide AI outputs, has become crucial for code generation, debugging, and vulnerability detection tasks. The study addresses three key research questions: identifying the most influential aspects of prompt engineering on CRUD efficiency compared to traditional coding, recognising common error patterns in AI-generated code and preemptive mitigation strategies, and leveraging human-AI collaboration to optimise CRUD application development. This study evaluates 52 relevant papers from 2018 to 2023 through a systematic literature review from Google Scholar, ResearchGate, arXiv, and Sci-Hub. The findings indicate that effective, prompt engineering significantly enhances productivity, reduces development time, and improves code adaptability to complex data structures. Techniques such as role-prompting and chain-of-thought prompting are highlighted for their ability to produce high-quality code, reducing errors and enhancing overall code quality. The review revealed significant results, including identifying prompt engineering techniques that substantially improve the performance of LLMs in CRUD operations. ChatGPT, in particular, excels in generating and debugging code but struggles with more complex tasks and security vulnerabilities. The study emphasises the need for continuous human oversight to ensure security and correctness, addressing challenges such as managing programming variability and the stochastic nature of LLMs.The research concludes that integrating ChatGPT into CRUD operations significantly advances software development, improving efficiency, code quality, and security. However, it is crucial to address its limitations through better prompt engineering and continuous human involvement. Future work will involve developing a fully functional CRUD application using generative AI, further evaluating its practical applications and limitations in software development.