Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippets

The research applies AI-driven code assistants to analyze a selection of influential computer code that has shaped modern technology, including email, internet browsing, robotics, and malicious software. The original contribution of this study was to examine half of the most significant code advances in the last 50 years and, in some cases, to provide notable improvements in clarity or performance. The AI-driven code assistant could provide insights into obfuscated code or software lacking explanatory commentary in all cases examined. We generated additional sample problems based on bug corrections and code optimizations requiring much deeper reasoning than a traditional Google search might provide. Future work focuses on adding automated documentation and code commentary and translating select large code bases into more modern versions with multiple new application programming interfaces (APIs) and chained multi-tasks. The AI-driven code assistant offers a valuable tool for software engineering, particularly in its ability to provide human-level expertise and assist in refactoring legacy code or simplifying the explanation or functionality of high-value repositories. NTRODUCTION The latest generation of artificial intelligence (AI) and chat applications [1-13] shows particular promise as software generators [4,11], presenting a new interactive way to learn complex coding principles [6], comment on existing code in multiple languages [8], and generally serve as coding assistants [8-12]. Recent efforts by OpenAI have put large language models (LLMs) into public access [1-2]. As an experimental platform, particularly for understanding software principles, its interactive chat [1] simulates a vast knowledge base, expert role-playing, and long-term memory spanning 8000 tokens, or approximately 20-25 pages of generated text. Several tests or benchmarks, such as QuixBugs [8] and HackerRank [12], have demonstrated the potential of generative coders as software assistants [10]. A recent review from the University of Washington and Microsoft Research [14] estimated that 1.2 million coders currently use OpenAI's copilot for tasks formerly requiring searches, such as code completion, commentary, or bug detection.