A random code generation method based on syntax tree layering model

Yu, Ping; Shu, Hui; Xiong, Xiaobing; Kang, Fei

doi:10.1117/12.2624688

Cited by 1 publication

(3 citation statements)

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“…Additionally, traditional methods incorporate domain-specific languages (DSLs) or modeling languages [20], providing expressive, domainspecific syntax for articulating code generation requirements. The code generation system translates these high-level specifications into actual code in the target programming language.…”

Section: Traditional Approachesmentioning

confidence: 99%

“…Adaptability Capability of the technique to adapt to changing code requirements or contexts [20], [21], [40].…”

Section: Efficiencymentioning

confidence: 99%

“…The significance of these AIT is evident in the acceleration of software development and the reduction of programming efforts [17], [18]. However, challenges remain, including evaluating the performance of AI-based code generators and ensuring the quality of the generated code [19], [20].…”

Section: Introductionmentioning

confidence: 99%

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

Odeh,

Mohammed

2024

TEM Journal

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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.

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Section: Traditional Approachesmentioning

confidence: 99%

“…Adaptability Capability of the technique to adapt to changing code requirements or contexts [20], [21], [40].…”

Section: Efficiencymentioning

confidence: 99%