Evaluation of Large Language Models in Object-Oriented Programming Assignments

Core Concepts

The author investigates the performance of GPT-3.5, GPT-4, and Bard in solving real-world object-oriented programming exercises, highlighting the models' ability to generate code but their shortcomings in adhering to OOP best practices.

Abstract

The study explores the use of Large Language Models (LLMs) like GPT-3.5, GPT-4, and Bard in solving object-oriented programming assignments. While these models can provide solutions to exercises, they often struggle with implementing industry best practices for OOP. The research emphasizes the importance of code quality and supervision when utilizing LLMs in educational settings.
Key points:

LLMs like GPT-3.5, GPT-4, and Bard were tested on real-world OOP exercises.
The models could generate working solutions but often overlooked OOP best practices.
GPT-4 outperformed GPT-3.5 and Bard but still required guidance for optimal solutions.
Recommendations include focusing on code quality evaluations and integrating LLMs into pedagogical settings with caution.

Stats

Contrary to introductory programming exercises, there exists a research gap with regard to the behavior of LLMs in OOP contexts.
In this study, three prominent LLMs - GPT-3.5, GPT-4, and Bard - were experimented with to solve real-world OOP exercises used in educational settings.
The findings revealed that while the models frequently achieved mostly working solutions to the exercises, they often overlooked the best practices of OOP.
GPT-4 stood out as the most proficient model followed by GPT-3.5 with Bard trailing last.

Quotes

"Contrary to introductory programming exercises, there exists a research gap with regard to the behavior of LLMs in OOP contexts."
"We advocate for a renewed emphasis on code quality when employing these models."
"While GPT-4 showcases promise, the deployment of these models in OOP education still mandates supervision."

Key Insights Distilled From

LLMs Still Can't Avoid Instanceof

by Bruno Pereir... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06254.pdf

Deeper Inquiries

How can educators effectively integrate LLMs into coding education while ensuring adherence to industry standards?

Educators can effectively integrate Large Language Models (LLMs) into coding education by incorporating them as tools for students to practice and learn from. To ensure adherence to industry standards, educators should focus on teaching students not only how to use LLMs for code generation but also how to critically evaluate the generated code. This involves emphasizing the importance of following best practices in software development, such as proper documentation, modular design, and efficient algorithms.
One approach is to pair LLM exercises with manual code reviews where students analyze the generated code for compliance with OOP principles and industry standards. By providing feedback on both functional correctness and code quality, educators can guide students towards producing high-quality solutions that align with professional expectations.
Additionally, educators should create assignments that challenge students to apply their understanding of object-oriented programming concepts in conjunction with using LLMs. Encouraging a blend of manual problem-solving and automated assistance through LLMs will help students develop a well-rounded skill set that combines technical proficiency with critical thinking abilities.

What are potential risks associated with relying too heavily on LLMs for generating code solutions?

Relying too heavily on Large Language Models (LLMs) for generating code solutions poses several risks that educators need to be aware of:

Overlooking Best Practices: LLM-generated code may prioritize functionality over adhering to best practices in software development. Students might become accustomed to suboptimal coding patterns or shortcuts suggested by the models without fully understanding the underlying principles.

Limited Understanding: Excessive reliance on LLMs could lead to a surface-level understanding of programming concepts among students. They may struggle when faced with real-world scenarios that require deeper knowledge and problem-solving skills beyond what the models provide.

Lack of Creativity: Constantly using pre-built solutions from LLMs may hinder students' creativity in designing unique approaches or innovative solutions independently. This dependency could limit their ability to think outside the box and explore alternative strategies.

Security Concerns: Automatically accepting all suggestions from an LLM without scrutiny can introduce security vulnerabilities or inefficient coding practices into projects, potentially compromising system integrity or performance.

To mitigate these risks, it's essential for educators to balance the use of LLMs with traditional learning methods, encourage critical thinking alongside automated assistance, and emphasize hands-on practice combined with thoughtful reflection on generated solutions.

How might students benefit from engaging with LLM-generated code through project-based learning approaches?

Engaging with Large Language Model (LLM)-generated code through project-based learning approaches offers several benefits for students:

Hands-On Application: Project-based learning allows students to apply theoretical knowledge practically by working on real-world projects where they utilize both their own problem-solving skills and insights gained from interacting with LMM-generated solutions.

Enhanced Problem-Solving Skills: By tackling complex projects that require integrating various components developed partially through interactions with LLMS,students develop critical thinking abilities,such as analyzing requirements,breaking down tasks,and debugging issues efficiently.

3 .Exposureto Industry Standards: Through project work involving LLMS ,students gain exposureto currentindustry trendsandbestpracticesinsoftwaredevelopment.Theylearnhowtointegrateautomatedtoolswhilemaintainingcodequalityandadherencetoindustrystandards.
4 .Collaboration Opportunities: Project-basedlearningencouragescollaborationamongstudentsas theyworkonlargerscaleassignmentsrequiringdivisionoflabortasks.This fosterscommunicationteamworkskillsessentialforprofessionalsettings.
5 .Portfolio Development: EngagingwithprojectsthatincorporateLLM-assistedcodingallowsstudentstobuildaportfolioofdiverseprojects showcasingtheirabilitiesinbothmanualcodingandinleveragingautomatedtools.Thiscanbevaluablewhenapplyingforinternshipsorjobsinthefuture.
By combining project-based learning methodologieswiththeuseofLargeLanguageModels,Learningexperiencesbecomeholistic,challenging,andrewardingforstudents,enablingthemtogrowasaftwaredeveloperscapableofnavigatingthecomplexitiesoftoday’sdigitallandscape