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

2024-08-18 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A study involving 91 participants in a simulated Stack Overflow environment investigated how code comments influence the perceived helpfulness of answers. The research found that both block and inline comments significantly increase perceived helpfulness compared to uncommented code. Specifically, block comments were rated as more helpful than inline comments, an effect particularly pronounced among novices. Interestingly, traditional surface features like answer position and score had no significant impact on helpfulness ratings. This contrasts with prior research and suggests users prioritize content quality. The findings have implications for improving community-driven platforms like Stack Overflow, which has over 24 million questions and 36 million answers, and for guiding AI-based coding assistants like GitHub Copilot in generating more readable code.

Key takeaway

For software developers contributing to Q&A platforms or designing AI coding assistants, prioritize comprehensive code documentation. Your code snippets should include block comments, as these are perceived as most helpful, especially by less experienced programmers. This approach enhances code comprehensibility and reuse, mitigating risks associated with poorly understood code. Additionally, when evaluating solutions, focus on the content's quality rather than relying on superficial cues like answer position or score.

Key insights

Code comments, especially block comments, significantly enhance the perceived helpfulness of Stack Overflow code snippets.

Principles

• Commented code is more helpful than uncommented code.
• Block comments are more helpful than inline comments.
• Novices benefit more from block comments.

Method

An online experiment with 91 participants simulated Stack Overflow, varying comment type (block, inline, none), answer position, and score. Participants rated answer helpfulness.

In practice

• Prioritize block comments for code explanations.
• Prompt AI tools to generate block comments.
• Focus on content quality over platform heuristics.

Topics

• Code Comments
• Stack Overflow
• Code Comprehension
• Q&A Platforms
• AI Coding Assistants
• Software Development
• User Experience

Best for: AI Product Manager, Research Scientist, Software Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.