Research topic
Report
Detailed summary
Several papers discuss generative AI for creating agent-like pipelines that manage entire codebases, including key aspects like architecture design, training methodologies, real-world applications, and performance metrics.
For instance, AutoCoder [1] excels in code evaluation through an agent interaction and execution-verified tuning approach, showing notable performance on the HumanEval benchmark. CodeAgent [3] details an LLM-based framework specifically for repository-level code generation with improved performance on real-world tasks. AutoDev [5] presents a comprehensive AI framework for automated software development, covering autonomous codebase management tasks. These papers collectively highlight the progression in employing generative AI to advance coding assistants capable of managing complete software development processes.
Categories of papers
The most important categories to highlight are those that directly address using generative AI for agent-like pipelines and coding assistants managing entire codebases, followed by categories focusing on architecture design, training methodologies, real-world applications, and performance metrics. This structured approach aids in easily identifying relevant research that meets the specified criteria of complex, real-world coding assistant systems.
Generative AI for Agent-Like Pipelines/Coding Assistants Managing Entire Codebases Description: Studies focused on utilizing generative AI to create agent systems capable of managing entire software development processes, including various coding tasks across a codebase. References: [1, 3, 5, 6, 7, 9, 11, 16, 21, 36]
Architectural Design of Generative AI Systems Description: Papers detailing the architectural frameworks and system designs for implementing generative AI in coding assistants and agent-like pipelines. References: [5, 6, 11, 14, 26, 30, 56]
Training Methodologies for Generative AI in Software Development Description: Research focused on the training methods for AI models used in code generation, including self-supervised learning, few-shot learning, and execution-verified tuning. References: [1, 6, 8, 13, 19, 34, 44, 75]
Performance Metrics and Real-World Applications Description: Studies providing evaluation metrics and real-world application examples for AI-driven coding assistants, including benchmarks like HumanEval and practical deployment reports. References: [1, 3, 7, 19, 21, 24, 33, 36]
These categories encapsulate the core areas of interest and present a concise guide to the most pertinent research.