Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generation

Core Concepts

Large language models like ChatGPT show promise in code generation, but there are limitations in their practical application for developers.

Abstract

Large language models (LLMs) have shown proficiency in code generation, but their real-world effectiveness remains unclear. The study evaluates interactions between developers and ChatGPT using the DevGPT dataset. Findings suggest that LLM-generated code is often used for high-level concepts or examples rather than production-ready code. The study highlights the need for further improvements before LLMs can be widely integrated into software development. Key research questions focus on developer interactions with ChatGPT and the usefulness of generated code. The study provides insights into how developers utilize ChatGPT and the challenges faced in integrating AI-generated code into real-world projects.

Stats

DevGPT dataset comprised 17,913 prompts and 11,751 code snippets.
Commit conversations required an average of 2.4 prompt-response rounds.
65.3% of conversations were related to code generation.

Quotes

"Developers primarily use ChatGPT for requesting improvements and tend to avoid additional code generation within the same conversation to prevent confusion."
"In most cases, the generated code is Supplementary Info to developers, perhaps due to inferior quality."
"32.8% of generated code is not used, emphasizing the need for further exploration of AI-generated code."

Key Insights Distilled From

Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generation

by Kailun Jin,C... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2402.11702.pdf

Deeper Inquiries

How can developers effectively leverage LLMs like ChatGPT beyond high-level concepts?

Developers can effectively leverage Large Language Models (LLMs) like ChatGPT beyond high-level concepts by incorporating them into their workflow for tasks such as code completion, source code mapping, and system maintenance. To do this effectively, developers should provide clear and specific prompts to the LLMs, guiding them towards generating accurate and relevant code snippets. By understanding the capabilities and limitations of LLMs, developers can optimize their interactions with these models to enhance productivity in software development tasks.

What are potential drawbacks or risks associated with relying on AI-generated code in software development?

There are several potential drawbacks and risks associated with relying on AI-generated code in software development:

Quality Concerns: The quality of AI-generated code may vary, leading to bugs or inefficiencies if not thoroughly reviewed.
Security Risks: AI-generated code may inadvertently introduce security vulnerabilities if not properly validated.
Lack of Understanding: Developers may become overly reliant on AI-generated solutions without fully understanding the underlying logic or implications.
Maintenance Challenges: Code generated by AI may be harder to maintain or modify compared to human-written code.
Ethical Considerations: There could be ethical concerns related to plagiarism or intellectual property rights when using AI-generated content.

How might survivorship bias impact the conclusions drawn from the DevGPT dataset?

Survivorship bias in the DevGPT dataset could lead to skewed conclusions due to certain conversations being retained while others are omitted. In this context, it could mean that only resolved discussions where developers found value in ChatGPT's responses were included in the dataset, potentially overlooking a broader range of developer interactions that did not result favorably. This bias might overemphasize positive outcomes and usability metrics while neglecting instances where ChatGPT was less effective or even detrimental in assisting developers with their tasks. As a result, conclusions drawn from such biased datasets may not accurately reflect real-world scenarios where challenges and failures also play a significant role in evaluating technology effectiveness.