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Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Programming Assignments

Q: How can the peer solution selection strategy be further improved to better identify the most relevant reference code for repairing advanced student assignments?

To enhance the peer solution selection strategy for identifying the most relevant reference code, several improvements can be implemented: Semantic Matching: Incorporate more advanced semantic matching techniques to compare the functionality and logic of the buggy code with potential peer solutions. This can involve analyzing the data flow, control flow, and variable dependencies to identify solutions that closely align with the buggy code. Contextual Understanding: Develop a deeper understanding of the context of the assignment and the specific requirements of the task to ensure that the selected peer solution not only fixes the bug but also aligns with the overall objective of the assignment. Machine Learning Models: Utilize machine learning models to learn from past successful repairs and identify patterns in the relationship between buggy code and effective peer solutions. This can help in predicting the most suitable reference code for a given bug. Feedback Loop: Implement a feedback loop mechanism where the effectiveness of selected peer solutions is evaluated based on the repair outcomes. This feedback can be used to continuously improve the selection process over time. Ensemble Methods: Combine multiple selection criteria, such as execution results, data flow analysis, and syntactic similarity, using ensemble methods to create a more robust and comprehensive peer solution selection process.

Q: How can the PaR framework be extended to handle more complex programming constructs, such as recursive functions or object-oriented programming features, that are commonly encountered in advanced programming courses?

To extend the PaR framework to handle more complex programming constructs encountered in advanced programming courses, the following strategies can be implemented: Enhanced Prompt Generation: Include detailed descriptions and examples of complex programming constructs like recursive functions or object-oriented programming features in the prompt. This will provide the LLM with the necessary context to understand and repair code involving these constructs. Specialized Training Data: Train the LLM on a diverse dataset that includes examples of programs with recursive functions, inheritance, polymorphism, and other object-oriented concepts. This will help the model learn the patterns and structures associated with these constructs. Fine-tuning: Fine-tune the LLM on specific tasks related to handling complex programming constructs. By providing targeted training on repairing code with recursive functions or object-oriented features, the model can improve its ability to address such challenges. Domain-Specific Knowledge: Incorporate domain-specific knowledge about advanced programming concepts into the prompt generation process. This can involve providing additional information about the specific constructs, their usage, and common errors associated with them. Iterative Development: Continuously iterate on the framework by testing and refining its performance on programs with complex constructs. Analyze the repair outcomes, identify areas of improvement, and adjust the framework accordingly to handle the intricacies of advanced programming assignments effectively.

核心概念

Peer-aided Repairer (PaR) is a novel framework that empowers large language models to effectively repair bugs in advanced student programming assignments by leveraging peer solutions and a multi-source prompt generation approach.

要約

The key highlights and insights from the content are:

The authors curated a new dataset called Defects4DS, which contains 682 submissions from 4 programming assignments of a higher-level programming course. The dataset features programs with increased complexity, longer lengths, and a variety of structures compared to introductory programming assignment datasets.
The authors analyzed the characteristics of the Defects4DS dataset and compared it to the ITSP dataset, a widely used introductory programming assignment dataset. The analysis revealed that the bugs in Defects4DS are more challenging to locate and fix due to the presence of complex grammatical components, related bugs, and a higher proportion of variable-related bugs.
To address the challenges in repairing advanced student assignments, the authors proposed the Peer-aided Repairer (PaR) framework. PaR works in three phases: Peer Solution Selection, Multi-Source Prompt Generation, and Program Repair.
- Peer Solution Selection identifies the closely related peer programs based on lexical, semantic, and syntactic criteria.
- Multi-Source Prompt Generation adeptly combines multiple sources of information, including the peer solution, program description, I/O-related information, and buggy code, to create a comprehensive and informative prompt for the Program Repair stage.
- The Program Repair stage feeds the generated prompt to a large language model, which then produces the fixed code.
The evaluation on Defects4DS and the ITSP dataset shows that PaR achieves a new state-of-the-art performance, demonstrating impressive improvements of 19.94% and 15.2% in repair rate compared to prior state-of-the-art LLM- and symbolic-based approaches, respectively.

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統計

The authors report the following key statistics:

The average and median number of lines of code in Defects4DS is 55 and 78, respectively, much higher than the 22 average and 20 median in the ITSP dataset.
38.6% of the Defects4DS programs contain complex grammatical components (struct, pointer, multi-dimensional array), while none are present in the ITSP dataset.
42.7% of the Defects4DS programs contain custom functions, compared to 20.5% in the ITSP dataset.

引用

"Automated Program Repair (APR) techniques can automatically generate patches to correct code errors by reasoning about the code semantics based on the given specification."
"Recent advancements in the development of Large Language Models (LLMs) provide an alternative solution for bug repair that does not necessitate experts with program analysis/repair experience."

抽出されたキーインサイト

Peer-aided Repairer

by Qianhui Zhao... 場所 arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01754.pdf

深掘り質問

How can the peer solution selection strategy be further improved to better identify the most relevant reference code for repairing advanced student assignments?

To enhance the peer solution selection strategy for identifying the most relevant reference code, several improvements can be implemented:

Semantic Matching: Incorporate more advanced semantic matching techniques to compare the functionality and logic of the buggy code with potential peer solutions. This can involve analyzing the data flow, control flow, and variable dependencies to identify solutions that closely align with the buggy code.

Contextual Understanding: Develop a deeper understanding of the context of the assignment and the specific requirements of the task to ensure that the selected peer solution not only fixes the bug but also aligns with the overall objective of the assignment.

Machine Learning Models: Utilize machine learning models to learn from past successful repairs and identify patterns in the relationship between buggy code and effective peer solutions. This can help in predicting the most suitable reference code for a given bug.

Feedback Loop: Implement a feedback loop mechanism where the effectiveness of selected peer solutions is evaluated based on the repair outcomes. This feedback can be used to continuously improve the selection process over time.

Ensemble Methods: Combine multiple selection criteria, such as execution results, data flow analysis, and syntactic similarity, using ensemble methods to create a more robust and comprehensive peer solution selection process.

How can the PaR framework be extended to handle more complex programming constructs, such as recursive functions or object-oriented programming features, that are commonly encountered in advanced programming courses?

To extend the PaR framework to handle more complex programming constructs encountered in advanced programming courses, the following strategies can be implemented:

Enhanced Prompt Generation: Include detailed descriptions and examples of complex programming constructs like recursive functions or object-oriented programming features in the prompt. This will provide the LLM with the necessary context to understand and repair code involving these constructs.

Specialized Training Data: Train the LLM on a diverse dataset that includes examples of programs with recursive functions, inheritance, polymorphism, and other object-oriented concepts. This will help the model learn the patterns and structures associated with these constructs.

Fine-tuning: Fine-tune the LLM on specific tasks related to handling complex programming constructs. By providing targeted training on repairing code with recursive functions or object-oriented features, the model can improve its ability to address such challenges.

Domain-Specific Knowledge: Incorporate domain-specific knowledge about advanced programming concepts into the prompt generation process. This can involve providing additional information about the specific constructs, their usage, and common errors associated with them.

Iterative Development: Continuously iterate on the framework by testing and refining its performance on programs with complex constructs. Analyze the repair outcomes, identify areas of improvement, and adjust the framework accordingly to handle the intricacies of advanced programming assignments effectively.