Does Location Influence Coding Practices? A Cross-Regional Study on Stack Overflow Code Quality

Developers routinely integrate Stack Overflow code snippets into their codebases. However, the quality of snippets embedded in users’ answers remain elusive, and existing evaluations of code quality tend to be language or context-specific. Moreover, literature have found that contribution patterns vary depending on geographical locales, creating an unexplained rift between code quality, user location, and latent contextual regional factors. The proposed study evaluates the quality of SQL, JavaScript, Python, Ruby, and Java snippets across reliability, readability, performance, and security dimensions, benchmarking findings across states in the USA and investigating how different diversity indicators correlate against code quality violations. The study culminates in a series of inductive content analyses that qualitatively supplement prior quality dimensions. This replication package is provided for those interested in further examining our research methodology.开发者通常会将堆栈溢出（Stack Overflow）平台中的代码片段集成至自身代码库中。然而，内嵌于用户回答中的代码片段其质量始终难以界定，且现有代码质量评估方法往往仅适用于特定编程语言或特定上下文场景。此外，已有研究文献发现，代码贡献模式会随地理区域的不同而存在差异，这使得代码质量、用户所在地与潜在上下文区域因素之间形成了尚未得到合理解释的割裂关系。 本研究针对SQL、JavaScript、Python、Ruby及Java五类代码片段，从可靠性、可读性、性能与安全性四个维度开展质量评估，在美国各州范围内对标本次研究结果，并探究各类多样性指标与代码质量违规情况之间的相关性。本研究最终通过一系列归纳式内容分析，从定性维度补充了此前已有的代码质量评估维度体系。 本可复现研究包（replication package）面向有意进一步探究本研究方法论的人员提供。