AI-Powered Code Helper for Intelligent Code Analysis, Debugging, and Multi-Language Execution

Key findings from this study

• The study found that AI-generated explanations improve code comprehension when integrated with execution and error detection in unified platforms.
• The researchers demonstrate that debugging time decreases when users access human-readable explanations of error root causes alongside syntax detection.
• The authors report that the system enhanced learning effectiveness for beginner programmers across multiple programming languages.

Overview

The study presents an AI-powered web-based system integrating code execution, error detection, and AI-generated explanations across Python, Java, and C++. The platform addresses limitations in traditional IDEs by combining syntax and logical error detection with human-readable explanations of code logic and debugging guidance.

Methods and approach

The system architecture unifies secure code execution, syntax and logical error detection, and AI-generated explanations within a single interface. The implementation supports multiple programming languages and generates explanations targeting comprehension of program logic and error root causes.

Results

Experimental evaluation demonstrated measurable improvements in code comprehension among users. Debugging time decreased relative to baseline tools, and learning effectiveness metrics showed enhancement compared to traditional IDE workflows. The unified platform successfully executed code across supported languages while providing contextual explanations beyond standard error reporting.

Implications

The integration of AI-generated explanations into development environments addresses a documented gap in beginner programmer support. Educational institutions deploying such systems may observe reduced cognitive load during error resolution and improved understanding of program logic. The approach suggests broader applicability for developer tools targeting knowledge transfer alongside functional code execution.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.