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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Recent Advances in Intelligent Source Code Generation: A Survey on Natural Language Based Studies.

Chen Yang¹, Yan Liu¹, Changqing Yin¹

¹School of Software Engineering, Tongji University, Shanghai 201804, China.

Entropy (Basel, Switzerland)

|September 28, 2021

Summary

This study systematically reviews Natural Language-based Source Code Generation (NLSCG), identifying challenges and opportunities for advancing automated software engineering. It proposes a structured approach to NLSCG research for future development.

Keywords:

machine learning application natural language-based source code generation systematic literature review

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Area of Science:

Software Engineering
Artificial Intelligence
Natural Language Processing

Background:

Source Code Generation (SCG) automates software development by converting descriptions into executable code.
Natural Language-based Source Code Generation (NLSCG) is a challenging SCG subfield due to natural language's abstraction.
Existing NLSCG studies often lack systematic analysis and face dataset-specific limitations.

Purpose of the Study:

To systematically review and analyze the field of Natural Language-based Source Code Generation (NLSCG).
To identify research gaps, technical challenges, and potential improvement directions in NLSCG.
To provide a foundational understanding and technical context for future NLSCG research.

Main Methods:

Systematic literature survey and tool research.
Snowballing process for study selection and analysis.
Empirical positioning of NLSCG within SCG genres using domain knowledge.

Main Results:

NLSCG faces challenges in bridging natural language processing and source code analytics.
Identified diverse technical schools and customization issues in current NLSCG studies.
Outlined key research dimensions, technical utilities, and a bounded technical context for NLSCG.

Conclusions:

A systematic approach is needed to advance NLSCG research beyond dataset-specific solutions.
Understanding the technical landscape and challenges is crucial for future NLSCG model development.
This review provides a foundation for further exploration and improvement in NLSCG.