The field of software development is rapidly evolving with the integration of Artificial Intelligence (AI) and Large Language Models (LLMs). Recent studies have demonstrated the potential of LLMs in assisting software development tasks, such as code generation, bug fixing, and code explanation. The use of LLMs has shown promise in improving the efficiency and accuracy of these tasks. Notably, the development of frameworks like P4OMP, RAILS, and QLPro has enabled the effective integration of LLMs with software development tools, resulting in improved code generation and bug fixing capabilities. Furthermore, the application of LLMs in code explanation and code search has also shown significant improvements. Overall, the field is moving towards the development of more advanced and specialized AI-assisted software development tools. Noteworthy papers in this area include P4OMP, which achieves 100% compilation success in generating OpenMP-annotated parallel code, and RAILS, which outperforms baseline prompting in preserving intent and avoiding hallucinations. QLPro is also notable for its ability to detect 41 confirmed vulnerabilities in open-source projects, including 6 previously unknown vulnerabilities.
Advances in AI-Assisted Software Development
Sources
Repair Ingredients Are All You Need: Improving Large Language Model-Based Program Repair via Repair Ingredients Search
Comparative Analysis of the Code Generated by Popular Large Language Models (LLMs) for MISRA C++ Compliance