Improving Large Language Model Classification of Logical Errors by Integrating Error Relationship into Prompts

Kernekoncepter

Improving the classification accuracy of logical errors in programming code by leveraging the relationships between different error types in Large Language Model prompts.

Resumé

The paper presents a comprehensive approach to classifying and augmenting logical errors in programming code using Large Language Models (LLMs). The key highlights are:

1. Defining ten types of logical errors and establishing their relationships to address potential confusion and ambiguity in error classification.
2. Proposing a new method for detecting logical errors using LLMs that incorporates the relationships between error types in the Chain-of-Thought and Tree-of-Thought prompts.
3. Demonstrating that the classification accuracy improves by 21% when the error relationship information is included in the prompts, compared to when it is not provided.
4. Introducing a methodology for generating a logical error dataset by augmenting correct code using LLMs, which can be useful for various programming-related applications.

The authors expect that this work can assist novice programmers in identifying and correcting the causes of logical errors more effectively.

Statistik

The classification accuracy for GPT-3.5-turbo improved from 35% without error descriptions to 56% with error descriptions, a 21% increase.
The classification accuracy for GPT-4 with error descriptions reached 86%.
The False Positive Rate (FPR) decreased from 0.145 for GPT-3.5-turbo to 0.13 for GPT-4, indicating better error type distinction.
The augmentation process generated 111 code samples, with 49 "Right Augmentation" and 24 "Other types of logical errors".

Citater

"Detecting such errors and developing an approach for assisting the user holds educational potential."
"Understanding error messages is crucial for effective programming learning, and use of LLM based approaches could benefit programming novices."
"We expect that our work can assist novice programmers in identifying the causes of code errors and correct them more effectively."

Vigtigste indsigter udtrukket fra

Improving LLM Classification of Logical Errors by Integrating Error Relationship into Prompts

by Yanggyu Lee,... kl. arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19336.pdf

Dybere Forespørgsler

How can the proposed approach be extended to handle logical errors specific to different programming languages and paradigms, such as object-oriented programming or functional programming?

To extend the proposed approach to handle logical errors specific to different programming languages and paradigms, such as object-oriented programming or functional programming, several strategies can be implemented:

Language-specific Error Definitions: Develop language-specific error definitions that encompass the unique characteristics and common pitfalls of each programming language. For object-oriented programming, errors related to class inheritance, polymorphism, and encapsulation can be defined. Similarly, for functional programming, errors related to higher-order functions, immutability, and recursion can be included.

Paradigm-based Prompting: Tailor the prompting techniques to focus on the specific constructs and principles of each programming paradigm. For object-oriented programming, prompts can emphasize class hierarchies, method overriding, and encapsulation errors. In contrast, prompts for functional programming can highlight issues with pure functions, side effects, and recursion.

Model Training on Diverse Codebases: Train the model on a diverse set of codebases written in different programming languages and paradigms to enhance its understanding of language-specific error patterns. This exposure will enable the model to generalize better and identify logical errors across a wide range of contexts.

Fine-tuning for Language-specific Errors: Fine-tune the model on datasets containing logical errors specific to object-oriented programming, functional programming, or any other paradigm. By focusing on these specialized datasets, the model can learn to detect and classify errors unique to each programming paradigm more effectively.

Collaboration with Domain Experts: Collaborate with domain experts in object-oriented programming, functional programming, or specific languages to refine the error definitions, prompting techniques, and model architectures. Domain expertise can provide valuable insights into the nuances of logical errors in different paradigms and guide the development of a more robust system.

By incorporating these strategies, the proposed approach can be extended to effectively handle logical errors specific to different programming languages and paradigms, enhancing its applicability and accuracy in diverse programming contexts.

How can the generated logical error dataset be utilized to develop more comprehensive programming education tools and intelligent tutoring systems?

The generated logical error dataset can be leveraged in various ways to develop more comprehensive programming education tools and intelligent tutoring systems:

Error-specific Feedback: Integrate the dataset into programming education platforms to provide error-specific feedback to learners. By analyzing the common logical errors identified in the dataset, the system can offer targeted guidance on how to rectify these errors, enhancing the learning experience for students.

Automated Code Review: Utilize the dataset to train automated code review systems that can identify logical errors in student code submissions. By comparing student code against the dataset of known logical errors, the system can highlight areas of improvement and provide suggestions for error correction.

Personalized Learning Paths: Incorporate the dataset into intelligent tutoring systems to personalize learning paths for individual students based on their error patterns. By analyzing the types of logical errors a student frequently makes, the system can tailor programming exercises and explanations to address their specific weaknesses.

Curriculum Enhancement: Use the dataset to enhance programming curricula by incorporating real-world examples of logical errors into educational materials. By showcasing common mistakes and their resolutions, educators can better prepare students to identify and troubleshoot logical errors in their code.

Research and Development: Share the dataset with researchers and developers in the field of programming education to facilitate further research on error analysis, classification, and remediation. The dataset can serve as a valuable resource for advancing the understanding of logical errors in programming and improving educational tools and methodologies.

By leveraging the generated logical error dataset in these ways, programming education tools and intelligent tutoring systems can become more effective, adaptive, and supportive in guiding students through the learning process and enhancing their programming skills.