A Comparative Review of AI Techniques for Automated Code Generation in Software Development: Advancements, Challenges, and Future Directions
A Comparative Review of AI Techniques for Automated Code Generation in Software Development: Advancements, Challenges, and Future Directions
Author(s): Ayman Odeh, Nada Odeh, Abdul Salam MohammedSubject(s): Information Architecture, Electronic information storage and retrieval
Published by: UIKTEN - Association for Information Communication Technology Education and Science
Keywords: Artificial intelligence; automated code generation; deep learning; evolutionary algorithms; machine learning; natural language processing
Summary/Abstract: Artificial Intelligence (AI), as one of the most important fields of computer science, plays a significant role in the software development life cycle process, especially in the implementation phase, where developers require considerable effort to convert software requirements and design into code. Automated Code Generation (ACG) using AI can help in this phase. Automating the code generation process is becoming increasingly popular as a solution to address various software development challenges and increase productivity. In this work, we provide a comprehensive review and discussion of traditional and AI techniques used for ACG, their challenges, and limitations. By analysing a selection of related studies, we will identify all AI methods and algorithms used for ACG, extracting the evaluation metrics and criteria such as Accuracy, Efficiency, Scalability, Correctness, Generalization, and more. These criteria will be used to perform a comparative result for AI methods used for ACG, exploring their applications, strengths, weaknesses, performance, and future applications.
Journal: TEM Journal
- Issue Year: 13/2024
- Issue No: 1
- Page Range: 726-739
- Page Count: 14
- Language: English
