Leveraging Large Language Models in Software Development Lifecycle - Banani Roy
AI Summary
Summary of Dr. Bon Roy’s Talk on Leveraging Large Language Models in Software Development Lifecycle
- Introduction to Dr. Bon Roy
- Assistant Professor, University of Saskatchewan
- Director of the Interactive Software Engineering and Analytics Lab
- Recognized for contributions to software development and mentoring.
- Research Focus
- Investigation into human-centric software development.
- Issues caused by software bugs and legacy software due to developer mistakes.
- The Role of AI in Software Development
- Acknowledged developer challenges in software maintenance.
- Importance of using AI tools (especially large language models (LLMs)) to reduce bugs and migration issues.
- Large Language Models (LLMs)
- Overview of capabilities in software engineering, including:
- Requirement analysis
- Code generation and completion
- Bug detection
- Test case generation.
- Research Contributions
- User Story Generation: Introduced a new prompt engineering technique called Refine and Thought (RAT) for generating user stories from requirement documents.
- Autogenics: Developed a method for generating inline comments for code snippets using LLMs to enhance code comprehension.
- Recognition of Effectiveness
- Presented results from evaluations indicating improvements in user story and inline comment quality.
- Highlighted the need for further studies to expand on findings and increase dataset comprehensiveness.
- Conclusion and Future Work
- Emphasized the necessity for fine-tuned LLMs to prevent hallucinations and improve performance in software engineering tasks.
- Plans to continue exploring AI’s role in enhancing developer productivity and code quality.
- Questions & Engagement
- Open floor for audience questions regarding LLM methodologies and developer engagement in software projects.