A Tertiary Review of Large Language Model-Based Code Generating Tasks: Trends, Challenges, and Future Directions
📰 ArXiv cs.AI
Learn how Large Language Models are used for code generation in software engineering and the trends, challenges, and future directions in this field
Action Steps
- Conduct a literature review of existing research on LLM-based code generating tasks to identify trends and challenges
- Analyze the applications and limitations of LLMs in code generation using tools like GitHub Copilot or Kite
- Evaluate the integration of LLMs into real-world development workflows and identify potential areas for improvement
- Develop and test LLM-based code generation models using frameworks like PyTorch or TensorFlow
- Compare the performance of different LLM architectures and fine-tuning techniques for code generation tasks
Who Needs to Know This
Software engineers, researchers, and developers can benefit from understanding the current state and future directions of LLM-based code generating tasks to improve their development workflows and stay up-to-date with the latest advancements
Key Insight
💡 LLMs have the potential to significantly improve code generation in software engineering, but their integration into real-world development workflows and potential limitations need to be carefully evaluated
Share This
🚀 LLMs are revolutionizing code generation in software engineering! 🤖 Learn about the trends, challenges, and future directions in this field 📚
Key Takeaways
Learn how Large Language Models are used for code generation in software engineering and the trends, challenges, and future directions in this field
Full Article
Title: A Tertiary Review of Large Language Model-Based Code Generating Tasks: Trends, Challenges, and Future Directions
Abstract:
arXiv:2605.25536v1 Announce Type: cross Abstract: Context. Large language models (LLMs) are increasingly applied to code-generating tasks (CGTs) in software engineering. While reported results are promising, the broader effects of such application and their integration into real-world development remain insufficiently understood with existing tertiary studies provide little in this area. Objective. This tertiary study consolidates secondary evidence on LLM-based CGTs, synthesizing the publicatio
Abstract:
arXiv:2605.25536v1 Announce Type: cross Abstract: Context. Large language models (LLMs) are increasingly applied to code-generating tasks (CGTs) in software engineering. While reported results are promising, the broader effects of such application and their integration into real-world development remain insufficiently understood with existing tertiary studies provide little in this area. Objective. This tertiary study consolidates secondary evidence on LLM-based CGTs, synthesizing the publicatio
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