Vol.13, No.1, February 2024.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

A Comparative Review of AI Techniques for Automated Code Generation in Software Development: Advancements, Challenges, and Future Directions


Ayman Odeh, Nada Odeh, Abdul Salam Mohammed


© 2024 Ayman Odeh, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 13, Issue 1, Pages 726-739, ISSN 2217-8309, DOI: 10.18421/TEM131-76, February 2024.


Received: 03 October 2023.

Revised:   17 January 2024.
Accepted: 23 January 2024.
Published: 27 February 2024.




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.


Keywords –Artificial intelligence, automated code generation, deep learning, evolutionary algorithms, machine learning, natural language processing.



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