The field of software engineering and hardware design is witnessing significant advancements with the application of Large Language Models (LLMs). Recent developments have focused on leveraging LLMs for automated code generation, code review, and bug report summarization. These innovations have the potential to improve the efficiency and accuracy of software development and hardware design workflows. Notable advancements include the use of LLMs for generating Verilog code, assessing RTL design specifications, and enhancing code review generation. Furthermore, research has explored the mitigation of hallucinations and omissions in LLMs for invertible problems, such as hardware logic design automation. Noteworthy papers in this area include: LAURA, which proposes an LLM-based review knowledge-augmented, context-aware framework for code review generation. Completion by Comprehension, a novel framework that enables code completion by comprehension of multi-granularity context from large-scale code repositories.
Advancements in Large Language Models for Software Engineering and Hardware Design
Sources
Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead
Assessing Large Language Models in Generating RTL Design Specifications
Progressive Code Integration for Abstractive Bug Report Summarization
LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLM
Feedback Loops and Code Perturbations in LLM-based Software Engineering: A Case Study on a C-to-Rust Translation System
Network Self-Configuration based on Fine-Tuned Small Language Models
Mitigating hallucinations and omissions in LLMs for invertible problems: An application to hardware logic design automation
Solving LLM Repetition Problem in Production: A Comprehensive Study of Multiple Solutions
Completion by Comprehension: Guiding Code Generation with Multi-Granularity Understanding