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

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arxiv 2402.08801 v2 pith:YJPZL45D submitted 2024-02-13 cs.SE cs.AI

Leuson Da Silva , Jordan Samhi , Foutse Khomh This is my paper · ORCID
classification cs.SEcs.AI
keywords llmsoverflowstackchatgptactivitychallengesdevelopmentempirical
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 3 Pith papers

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  2. Eradicating the Unseen: Detecting, Exploiting, and Remediating a Path Traversal Vulnerability across GitHub

    cs.CR 2025-05 conditional novelty 6.0 of 10

    A single vulnerable Node.js path traversal pattern was found in 1,756 GitHub projects, most rated critical, and the authors' automated pipeline produced patches, disclosures, and evidence that LLMs have learned the pattern.

  3. Explaining GitHub Actions Failures with Large Language Models: Challenges, Insights, and Limitations

    cs.SE 2025-01 conditional novelty 5.0 of 10

    A 31-developer survey found that LLM-generated explanations of GitHub Actions failures are perceived as correct and clear for simple logs, but less useful for complex CI/CD failures.

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