Code Generation - Leveraging Stack Overflow and GitHub Gists for Enhanced Training

Code generation has become a potent asset in software development, streamlining repetitive tasks and empowering developers to focus on higher-level priorities. Despite the remarkable progress of existing models, their training datasets often lack real-world context and a diverse array of code examples. This paper introduces an innovative code generation approach by harnessing Stack Overflow answers and GitHub Gists. Stack Overflow furnishes comprehensive contextual information around code snippets, while Gists offer a curated compilation of tailored solutions to specific problems. We hypothesize that merging these datasets will result in models generating code that is more akin to human-like, task-oriented, and efficient solutions. Our experiments affirm that this proposed approach surpasses baseline models trained on conventional code repositories, showcasing substantial advancements across several critical metrics. Additionally, we delve into an analysis of the generated code, emphasizing its heightened readability, maintainability, and applicability to specific tasks. Finally, we discuss the limitations encountered and chart potential future trajectories for this research, aiming to pave the way for even more robust and practical code generation models.