Does Editing Improve Answer Quality on Stack Overflow? A Data-Driven Investigation

• URL: http://arxiv.org/abs/2507.21329v1
• Date: Mon, 28 Jul 2025 20:51:55 GMT
• Title: Does Editing Improve Answer Quality on Stack Overflow? A Data-Driven Investigation
• Authors: Saikat Mondal, Chanchal K. Roy,
• Abstract summary: Poor-quality answers in technical Q&A platforms like Stack Overflow introduce inefficiencies, bugs, and security vulnerabilities.<n>To improve content quality, SO allows collaborative editing, where users revise answers to enhance clarity, correctness, and formatting.<n>We analyze 94,994 Python-related answers that have at least one accepted edit to determine whether edits improve semantic relevance, code usability, (3) code complexity, (4) security vulnerabilities, (5) code optimization, and (6) readability.
• Score: 5.176434782905268
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: High-quality answers in technical Q&A platforms like Stack Overflow (SO) are crucial as they directly influence software development practices. Poor-quality answers can introduce inefficiencies, bugs, and security vulnerabilities, and thus increase maintenance costs and technical debt in production software. To improve content quality, SO allows collaborative editing, where users revise answers to enhance clarity, correctness, and formatting. Several studies have examined rejected edits and identified the causes of rejection. However, prior research has not systematically assessed whether accepted edits enhance key quality dimensions. While one study investigated the impact of edits on C/C++ vulnerabilities, broader quality aspects remain unexplored. In this study, we analyze 94,994 Python-related answers that have at least one accepted edit to determine whether edits improve (1) semantic relevance, (2) code usability, (3) code complexity, (4) security vulnerabilities, (5) code optimization, and (6) readability. Our findings show both positive and negative effects of edits. While 53.3% of edits improve how well answers match questions, 38.1% make them less relevant. Some previously broken code (9%) becomes executable, yet working code (14.7%) turns non-parsable after edits. Many edits increase complexity (32.3%), making code harder to maintain. Instead of fixing security issues, 20.5% of edits introduce additional issues. Even though 51.0% of edits optimize performance, execution time still increases overall. Readability also suffers, as 49.7% of edits make code harder to read. This study highlights the inconsistencies in editing outcomes and provides insights into how edits impact software maintainability, security, and efficiency that might caution users and moderators and help future improvements in collaborative editing systems.

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