An Empirical Study of OpenAI API Discussions on Stack Overflow

TL;DR

This paper provides an empirical analysis of 2,874 Stack Overflow discussions to understand the challenges developers face when using OpenAI APIs, highlighting unique issues like prompt engineering and cost management.

Contribution

It is the first comprehensive empirical study analyzing developer challenges with OpenAI APIs through forum discussions, categorizing issues and providing actionable insights.

Findings

Identified nine categories of OpenAI API-related challenges

Analyzed the popularity and difficulty of API discussions

Proposed implications for developers and researchers

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.