Authors: Vipul Kanhere, Suraj Sonar, Atharva Awale, Pranav Shinde, Savita Biradar
Abstract: Software developers dedicate a substantial portion of their time to comprehending existing code, a challenge that intensifies as codebases grow in scale and complexity. Code Explanation Generators and Code Insight SaaS platforms have emerged as promising solutions, leveraging large language models to transform source code into accessible natural language explanations. This survey presents a comprehensive examination of code explanation technologies, tracing their evolution from traditional template-based and rule-based approaches through neural sequence models to contemporary LLM-powered systems. We establish a taxonomic framework for categorizing explanation tools across dimensions including target audience, explanation granularity, architectural approach, and deployment model. Our analysis encompasses commercial platforms, open-source implementations, IDE integrations, and lightweight web applications built on frameworks such as Streamlit that enable rapid development and free cloud deployment. The comparative analysis reveals significant consolidation around large language model approaches, with differentiation increasingly based on interface design, prompting strategies, and deployment architectures rather than fundamental algorithmic differences. Despite remarkable progress in explanation quality and accessibility, we identify persistent gaps including primitive granularity adaptation mechanisms, absent interpretability features for reliability assessment, inadequate privacy-preserving deployment options, limited contextual awareness beyond isolated code snippets, and evaluation methodologies that fail to capture developer-centric comprehension outcomes. Based on these findings, we propose future research directions encompassing improved evaluation frameworks grounded in task- based assessment, interpretable explanation generation with confidence indication, domain-specific adaptation for specialized contexts, and responsible deployment practices addressing privacy, accuracy, and equitable access. This survey provides structured guidance for researchers advancing code explanation capabilities and practitioners developing or adopting explanation tools.
