Code Commenting and Explanation for LLM-based Coders

Project Overview:

Objective

The goal was to develop a dataset that would enhance LLMs’ understanding of code logic, functions, and potential edge cases, thereby improving their utility in generating high-quality code comments and explanations for developers.

Scope

The dataset includes a diverse collection of code snippets from various programming languages and domains. Each snippet is annotated with detail explanations covering the logic, functionality, and potential edge cases, providing the LLM with the context needed to generate accurate and helpful comments.

Sources

Code Collection: A total of 100,000 code snippets were collect from a variety of programming languages and domains, ensuring broad coverage of coding practices and use cases.

Data Collection Metrics

Total Code Snippets Collected: 100,000 code snippets.
Explanations Provided: 100,000 detailed explanations, with an average length of 50 words per explanation.

Annotation Process

Stages

Expert Annotations: A team of 50 annotators with expertise in software development provided detail explanations for each code snippet. These explanations cover the logic, functionality, and potential edge cases to ensure comprehensive understanding.
Contextual Relevance: Annotations were design to be contextually relevant, helping the LLM grasp the nuances of each code snippet and generate appropriate comments.

Annotation Metrics

Team Involvement: A team of 50 annotators, all experience software developers and engineers, work over a period of 4 months to complete the project.
Total Annotations: 100,000 explanations were provided, ensuring that each code snippet was thoroughly explained.

Quality Assurance

Stages

Annotation Accuracy: Rigorous quality checks were implemented to ensure that the explanations were accurate, detailed, and contextually appropriate.
Consistency Reviews: Regular reviews were conducted to maintain consistency across all annotations, ensuring that the dataset was reliable and effective for training LLMs.

QA Metrics

Explanation Accuracy: High accuracy was achieved in providing detailed and contextually relevant explanations for each code snippet.
Consistency in Annotations: The dataset maintained a high level of consistency across annotations, contributing to the reliability of the LLM’s training data.

Conclusion

The creation of this code commenting and explanation dataset significantly enhanced the ability of LLMs to understand and generate accurate code explanations. This improvement has proven valuable for developers, enabling more effective use of LLM-base coding tools and improving the quality of auto-generate code comments.