Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

An Empirical Study of OpenAI API Discussions on Stack Overflow

Xiang Chen, Jibin Wang, Chaoyang Gao, Xiaolin Ju, Zhanqi Cui

TL;DR

The paper conducts the first large-scale empirical analysis of OpenAI API discussions on Stack Overflow, addressing how developers use and struggle with nine API categories. By collecting 2,874 posts, labeling them into nine categories, and applying topic modeling, the study reveals category-specific challenges (e.g., prompt design, cost management, context handling, and third-party integrations) and overall trends in popularity and difficulty. Key findings include GPT Actions being the most challenging category, Chat API dominating discussions, and distinct per-category topics that inform targeted improvements in documentation, versioning, and tooling. The authors propose actionable implications for developers, LLM vendors, and researchers, such as enhanced tutorials, robust deprecation policies, better context management, cost-monitoring practices, and knowledge-base construction, and they provide data and scripts on GitHub for reproducibility.

Abstract

The rapid advancement of large language models (LLMs), represented by OpenAI's GPT series, has significantly impacted various domains such as natural language processing, software development, education, healthcare, finance, and scientific research. However, OpenAI APIs introduce unique challenges that differ from traditional APIs, such as the complexities of prompt engineering, token-based cost management, non-deterministic outputs, and operation as black boxes. To the best of our knowledge, the challenges developers encounter when using OpenAI APIs have not been explored in previous empirical studies. To fill this gap, we conduct the first comprehensive empirical study by analyzing 2,874 OpenAI API-related discussions from the popular Q&A forum Stack Overflow. We first examine the popularity and difficulty of these posts. After manually categorizing them into nine OpenAI API-related categories, we identify specific challenges associated with each category through topic modeling analysis. Based on our empirical findings, we finally propose actionable implications for developers, LLM vendors, and researchers.

An Empirical Study of OpenAI API Discussions on Stack Overflow

TL;DR

The paper conducts the first large-scale empirical analysis of OpenAI API discussions on Stack Overflow, addressing how developers use and struggle with nine API categories. By collecting 2,874 posts, labeling them into nine categories, and applying topic modeling, the study reveals category-specific challenges (e.g., prompt design, cost management, context handling, and third-party integrations) and overall trends in popularity and difficulty. Key findings include GPT Actions being the most challenging category, Chat API dominating discussions, and distinct per-category topics that inform targeted improvements in documentation, versioning, and tooling. The authors propose actionable implications for developers, LLM vendors, and researchers, such as enhanced tutorials, robust deprecation policies, better context management, cost-monitoring practices, and knowledge-base construction, and they provide data and scripts on GitHub for reproducibility.

Abstract

The rapid advancement of large language models (LLMs), represented by OpenAI's GPT series, has significantly impacted various domains such as natural language processing, software development, education, healthcare, finance, and scientific research. However, OpenAI APIs introduce unique challenges that differ from traditional APIs, such as the complexities of prompt engineering, token-based cost management, non-deterministic outputs, and operation as black boxes. To the best of our knowledge, the challenges developers encounter when using OpenAI APIs have not been explored in previous empirical studies. To fill this gap, we conduct the first comprehensive empirical study by analyzing 2,874 OpenAI API-related discussions from the popular Q&A forum Stack Overflow. We first examine the popularity and difficulty of these posts. After manually categorizing them into nine OpenAI API-related categories, we identify specific challenges associated with each category through topic modeling analysis. Based on our empirical findings, we finally propose actionable implications for developers, LLM vendors, and researchers.
Paper Structure (25 sections, 2 equations, 13 figures, 2 tables)

This paper contains 25 sections, 2 equations, 13 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Methodology
  4. Data Collection
  5. Data Labeling
  6. Popularity Trend Analysis
  7. Difficulty Analysis
  8. Challenge Identification
  9. RQ1: Popularity Analysis
  10. RQ2: Difficulty Analysis
  11. RQ3: Challenge Identification
  12. Identified Topics for Chat API
  13. Identified Topics for Embeddings API
  14. Identified Topics for Audio API
  15. Identified Topics for Assistants API
  16. ...and 10 more sections

Figures (13)

  • Figure 1: Methodology overview of our empirical study.
  • Figure 2: Distribution of posts in nine OpenAI API categories.
  • Figure 3: The number of posts and users related to OpenAI APIs over time.
  • Figure 4: A sample post in the topic of API core operation errors.
  • Figure 5: A sample post in the topic of model migration.
  • ...and 8 more figures