Enhancing Binary Code Comment Quality Classification: Integrating Generative AI for Improved Accuracy
Project Overview
The document explores the integration of generative AI in education, specifically focusing on enhancing the quality of binary code comment classification. By augmenting a dataset of annotated code and comments with pairs generated by a Large Language Model (LLM), the study aims to improve the accuracy of classifying code comments, which is crucial for effective software development. The research highlights the significance of automated assessments of code comment quality and employs advanced machine learning techniques, particularly BERT, to evaluate the usefulness of these comments. Through this innovative approach, the findings suggest that generative AI can significantly enhance educational tools in programming and software engineering, leading to better learning outcomes and more efficient coding practices. Overall, the document underscores the transformative potential of generative AI in education, particularly in fostering improved understanding and application of code documentation.
Key Applications
Binary code comment quality classification model using generative AI
Context: Software development, target audience includes software engineers and researchers in software maintenance.
Implementation: Utilized generative AI to create additional code-comment pairs to augment the existing dataset for training a classification model.
Outcomes: Improved accuracy in classifying code comments as 'Useful' or 'Not Useful', enhancing maintainability and readability of software.
Challenges: Dependence on the quality of generated data and the challenge of ensuring that the model generalizes well across diverse datasets.
Implementation Barriers
Technical barrier
The challenge of integrating generated data without introducing noise or irrelevant information.
Proposed Solutions: Implement rigorous validation processes for generated data and manual curation of initial datasets.
Ethical barrier
Concerns regarding bias in the training data and generated outputs, which could affect the fairness of the models.
Proposed Solutions: Incorporate diverse datasets and continuously monitor for bias in model predictions.
Project Team
Rohith Arumugam S
Researcher
Angel Deborah S
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Rohith Arumugam S, Angel Deborah S
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai