LLMs and Stack Overflow Discussions: Reliability, Impact, and Challenges

TL;DR

This study empirically evaluates the reliability and impact of large language models like ChatGPT and LLaMA on Stack Overflow, revealing their strengths, limitations, and effects on user activity and platform dynamics.

Contribution

It provides a comprehensive analysis of LLMs' performance on technical questions, compares different models, and discusses implications for the future of developer support platforms.

Findings

LLMs challenge but do not fully replace human expertise.

Significant decline in Stack Overflow user activity observed.

LLMs have domain-specific strengths and failure modes.

Abstract

Since its release in November 2022, ChatGPT has shaken up Stack Overflow, the premier platform for developers queries on programming and software development. Demonstrating an ability to generate instant, human-like responses to technical questions, ChatGPT has ignited debates within the developer community about the evolving role of human-driven platforms in the age of generative AI. Two months after ChatGPT release, Meta released its answer with its own Large Language Model (LLM) called LLaMA: the race was on. We conducted an empirical study analyzing questions from Stack Overflow and using these LLMs to address them. This way, we aim to (i) quantify the reliability of LLMs answers and their potential to replace Stack Overflow in the long term; (ii) identify and understand why LLMs fail; (iii) measure users activity evolution with Stack Overflow over time; and (iv) compare LLMs together. Our empirical results are unequivocal: ChatGPT and LLaMA challenge human expertise, yet do not outperform it for some domains, while a significant decline in user posting activity has been observed. Furthermore, we also discuss the impact of our findings regarding the usage and development of new LLMs and provide guidelines for future challenges faced by users and researchers.