VADER-SC: A Model Agnostic Tool for Large Scale, AI Driven Automated Source Code Summarization

Production of a natural language description for the function of a source code segment is commonly referred to as source code summarization. Useful comments in source code can facilitate more rapid onboarding of new engineers and contribute to decreased maintenance costs. Unfortunately, the documentation task can also be labor intensive. In this paper, we introduce a new model agnostic tool for AI driven automation of source code summarization at scale. The initial version of the adVanced AI Driven Enhancement to Readability for Spurce Code (VADER-SC) software offers numerous options for customization and the ability to leverage a variety of AI models to enable experimentation in resource constrained environments, while also scaling up to benefit from larger models in contexts with increased compute resources. It further supports private cloud, self-hosted, and air-gapped network configurations for environments with strict intellectual property protections or processing of sensitive or controlled data. Qualitative and quantitative results suggest model selection, fine-tuning, and multi-shot tailoring significantly impact the quality of generated comments. VADER-SC could be an enabler for practitioners to explore large-scale automation of AI driven source code summarization and researchers may find it enables studies with larger volumes of disparate data across a diversity of AI model options and target programming languages.