The Influence of Code Comments on the Perceived Helpfulness of Stack Overflow Posts

TL;DR

This study shows that code comments significantly enhance the perceived helpfulness of Stack Overflow answers, especially for novices, and highlights implications for AI coding tools and community platforms.

Contribution

It provides empirical evidence on the impact of code comments on perceived helpfulness and discusses implications for AI tools and community-driven knowledge sharing.

Findings

Comments increase perceived helpfulness of answers

Novices find block comments more helpful than inline comments

Answer position and score are less influential

Abstract

Question-and-answer platforms such as Stack Overflow are an important way for software developers to share and retrieve knowledge. However, reusing poorly understood code can lead to serious problems, such as bugs or security vulnerabilities. To better understand how code comments affect the perceived helpfulness of Stack Overflow answers, we conducted an online experiment simulating a Stack Overflow environment (n=91). The results indicate that both block and inline comments are perceived as significantly more helpful than uncommented source code. Moreover, novices rated code snippets with block comments as more helpful than those with inline comments. Interestingly, other surface features, such as the position of an answer and its answer score, were considered less important. Moreover, the content of Stack Overflow has been a major source for training large language models. AI-based coding assistants such as GitHub Copilot, which are based on these models, are changing the way Stack Overflow is used. However, our findings have implications beyond Stack Overflow. First, they may help to improve the relevance also of other community-driven platforms, which provide human advice and explanations of code solutions, complementing AI-based support for software developers. Second, since chat-based AI tools can be prompted to generate code in different ways, knowing which properties influence perceived helpfulness can lead to more targeted prompting strategies to generate readable code snippets.