# An Empirical Study of Obsolete Answers on Stack Overflow

arXiv: https://arxiv.org/abs/1903.12282 · HTML: https://mlsanity.com/paper/1903.12282/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2019.

## Built on by (most cited first, 14 of 14)

- [Exploring and Unleashing the Power of Large Language Models in Automated Code Translation](https://mlsanity.com/paper/2404.14646/) (2024)
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- [Beyond Code Snippets: Benchmarking LLMs on Repository-Level Question Answering](https://mlsanity.com/paper/2603.26567/) (2026)
- [Studying and Recommending Information Highlighting in Stack Overflow Answers](https://mlsanity.com/paper/2401.01472/) (2024)
- [A Comprehensive Framework for Evaluating API-oriented Code Generation in Large Language Models](https://mlsanity.com/paper/2409.15228/) (2024)
- [Evaluating the Ebb and Flow: An In-depth Analysis of Question-Answering Trends across Diverse Platforms](https://mlsanity.com/paper/2309.05961/) (2023)
- [Large-Scale Analysis of Online Questions Related to Opioid Use Disorder on Reddit](https://mlsanity.com/paper/2504.08044/) (2025)
- [Why Attention Fails: A Taxonomy of Faults in Attention-Based Neural Networks](https://mlsanity.com/paper/2508.04925/) (2025)
- [RAG-Reflect: Agentic Retrieval-Augmented Generation with Reflections for Comment-Driven Code Maintenance on Stack Overflow](https://mlsanity.com/paper/2604.22217/) (2026)
- [On the Feasibility of Predicting Questions being Forgotten in Stack Overflow](https://mlsanity.com/paper/2110.15789/) (2021)
- [Skill over Scale: The Case for Medium, Domain-Specific Models for SE](https://mlsanity.com/paper/2306.03268/) (2023)
- [Human-Aligned Enhancement of Programming Answers with LLMs Guided by User Feedback](https://mlsanity.com/paper/2601.17604/) (2026)
- ["I Just Need GPT to Refine My Prompts": Rethinking Onboarding and Help-Seeking with Generative 3D Modeling Tools](https://mlsanity.com/paper/2603.29118/) (2026)

## Abstract (arXiv)

Stack Overflow accumulates an enormous amount of software engineering knowledge. However, as time passes, certain knowledge in answers may become obsolete. Such obsolete answers, if not identified or documented clearly, may mislead answer seekers and cause unexpected problems (e.g., using an out-dated security protocol). In this paper, we investigate how the knowledge in answers becomes obsolete and identify the characteristics of such obsolete answers. We find that: 1) More than half of the obsolete answers (58.4%) were probably already obsolete when they were first posted. 2) When an obsolete answer is observed, only a small proportion (20.5%) of such answers are ever updated. 3) Answers to questions in certain tags (e.g., node.js, ajax, android, and objective-c) are more likely to become obsolete. Our findings suggest that Stack Overflow should develop mechanisms to encourage the whole community to maintain answers (to avoid obsolete answers) and answer seekers are encouraged to carefully go through all information (e.g., comments) in answer threads.