AI Programming Assistant for Automated Code Repair and Feedback
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Solution Overview
Problem
In large-scale training centers, obtaining timely and efficient feedback on programming tasks is challenging, especially for novice programmers who struggle to understand and localize errors in code, as human tutoring is often infeasible and time-consuming.
Innovation Solution
An artificial intelligence-based intelligent programming assistance system that uses sequence-to-sequence networks and deep neural networks to provide automatic program repair, code completion, personalized feedback, and virtual question-answering, analyzing error messages and code to suggest corrections and next code tokens, and utilizing a learning-based technique to generate fixes for programming errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human tutoring is used to guide programmers through code development and error correction, then personalized feedback and guidance quality improve, but time consumption and scalability worsen
Solution Approach 1:
The system enables self-service by having the AI assistant autonomously analyze code errors, generate corrections, and provide feedback without requiring human tutor intervention. The AI processes code submissions, identifies errors, and delivers personalized guidance automatically, allowing students to receive immediate feedback independent of tutor availability.
Solution Approach 2:
The patent replaces the mechanical system of human tutoring with an AI-based automated system. The AI assistant substitutes human tutors by performing code analysis, error detection, and feedback generation through algorithmic processing, eliminating the need for manual code review while maintaining personalized feedback capabilities.
2Measurement precision
If human tutors review and correct student code manually, then error detection accuracy improves, but productivity and response time worsen
Solution Approach 1:
The AI system performs self-service code analysis by automatically detecting errors, suggesting corrections, and validating fixes without human intervention. This autonomous error detection capability maintains high accuracy while dramatically increasing the number of code submissions that can be reviewed per unit time.
Solution Approach 2:
The system changes the operational parameters of code review from manual human analysis to automated AI processing. This parameter change enables simultaneous analysis of multiple code submissions with consistent accuracy, transforming the throughput from one-code-per-tutor-to-many-codes-per-AI-system while maintaining detection precision.
3Adaptability or versatility
If traditional programming education with human tutors is used, then personalized learning support improves, but scalability to large student groups worsens
Solution Approach 1:
The AI assistant achieves universality by providing personalized learning support to unlimited numbers of students simultaneously. The system maintains adaptability to individual student needs through contextual analysis while scaling to serve entire classrooms or institutions, eliminating the one-tutor-to-one-student limitation.
Solution Approach 2:
Each student receives personalized feedback through autonomous AI analysis of their specific code and learning patterns. The system adapts to individual needs without requiring dedicated tutor attention, enabling personalized learning support to scale across large student populations independently.
Data Source
AI summary
In some examples, artificial intelligence-based intelligent programming assistance may include ascertaining, for a software program that is to be completed, code for the program, and identifying, based on an analysis of error message-based contextual information associated with the ascertained code, an error in the ascertained code. A resolution to the identified error may be generated, and the code may be modified to resolve the identified error, Based on an analysis of problem description-based contextual information associated with the ascertained code, a next token associated with the ascertained code may be generated, and used to generate further code for the program. A performance of a user may be analyzed with respect to generation of the code for the program to generate feedback for the user. A query associated with the program may be ascertained from the user, and classified by utilizing an ontology to generate a response to the query.