Workshop Topics

This workshop will discuss a broad variety of topics related to reasoning and planning with LLMs for code development, including but not limited to:

  • Code Generation with LLMs: Techniques and methodologies for utilizing LLMs to automatically generate code snippets, functions, or entire programs.
  • Code Completion and Auto-suggestion: Improvements and innovations in LLM-based code completion systems, including context-aware suggestions and intelligent code refactoring.
  • Natural Language Interfaces for Programming: Design and evaluation of natural language interfaces that leverage LLMs for translating human-readable descriptions or queries into executable code.
  • Code Understanding and Summarization: Applications of LLMs for code understanding, summarization, and documentation generation to enhance code comprehension and maintainability.
  • Program Analysis and Debugging: Leveraging LLMs for program analysis tasks such as bug detection, program slicing, and static code analysis.
  • Code Translation and Migration: Techniques for translating code between different programming languages or migrating legacy codebases using LLMs.
  • Code Optimization and Performance Tuning: Employing LLMs for automated code optimization, performance profiling, and resource utilization improvement.
  • Ethical and Responsible Use of LLMs in Code Development: Discussions on ethical considerations, biases, and fairness issues in using LLMs for code development, along with strategies for mitigating potential risks.
  • Collaborative Coding with LLMs: Exploration of collaborative coding environments that incorporate LLMs to facilitate teamwork, code review, and knowledge sharing among developers.
  • Human-Like Programming Assistance: Research on making LLM-based programming assistance more human-like, interactive, and responsive to developers' needs and preferences.
  • Long-Term Code Evolution and Maintenance: Longitudinal studies and methodologies for using LLMs to support code evolution, maintenance, and adaptation to changing requirements over time.
  • Real-World Applications and Case Studies: Practical applications and case studies showcasing the use of LLMs in real-world code development projects across various domains and industries.
  • Evaluation Metrics and Benchmarks: Development of standardized evaluation metrics, datasets, and benchmarks for assessing the performance and effectiveness of LLM-based code development approaches.
  • Interdisciplinary Perspectives on LLMs and Code Development: Cross-disciplinary research that combines insights from natural language processing, programming languages, software engineering, and cognitive science to advance LLM-based code development techniques.