arXiv subjects

Elijah Zolduoarrati

Publications and source records attributed to Elijah Zolduoarrati.

3 recordsLinked to original sources

arXiv2026

Geographic Variation in Stack Overflow Code Quality: Evidence from a Cross-Regional Study of Coding Practices

Developers frequently reuse Stack Overflow code snippets, yet the quality of these snippets remains unevenly understood, particularly across programming languages and geographic contexts. This study investigates code quality in Stack Overflow answers from contributors located in the United States, focusing on SQL, JavaScript, Python, Ruby, and Java snippets. We evaluate four quality dimensions: reliability, readability, performance, and security. Using language-specific linting and static analysis tools, we quantify violations across states and cities, compute violation densities to enable fair regional comparison, and examine relationships between code quality and state-level diversity indicators. We further conduct inductive content analysis on code snippets from California, Utah, and North Dakota to identify qualitative patterns in code quality violations. Results show that readability violations are the most prevalent across all languages, followed by reliability, performance, and security. Common issues include improper whitespace, inconsistent formatting, program-flow errors, inefficient resource use, unsanitised inputs, and insecure dynamic evaluation. Regional analysis indicates that major technology hubs produce more parsable snippets but do not necessarily exhibit higher violation densities. States with broader access to computing devices, Internet subscriptions, higher income, and more equitable wealth distribution tend to show fewer code quality violations. Qualitative findings suggest that established technology regions often produce more complex violations, while less mature technology regions display more fundamental errors. These findings highlight the socio-technical nature of code quality in community question-answering platforms and suggest that developers should exercise caution when reusing online code snippets.

cs.SE↗

arXiv2026

Why Do You Contribute to Stack Overflow? Understanding Cross-Cultural Motivations and Usage Patterns before the Age of LLMs

Understanding motivations of contributors for participating in community question and answer platforms is crucial for sustaining knowledge-sharing ecosystem, which is necessary to advance the discipline while also ensuring its longevity. This is particularly necessary in the age of LLMs, where data from such portals are used to train these models. Limited insights exist regarding how motivations of contributors vary across different national cultures. This research investigates Stack Overflow contributor motivations, analysing regional differences and relations to platform activity. We combined qualitative content analysis of Stack Overflow profiles with quantitative linguistic analysis of data from the United States, China, and Russia. Using deductive content analysis, we identified 17 motivational categories. We applied correlation analysis to identify associations between stated motivations and platform activities. Contributors are primarily motivated by advertising opportunities and altruistic problem solving desires. American contributors demonstrated stronger self promotional behaviours while Chinese contributors exhibited greater learning oriented engagement. Our correlation analysis showed that those with more detailed profiles tend to engage in advertising and social activities, while learning oriented users maintain minimal self presentation. Understanding these variations can inform strategies for enhancing cross cultural participation in software engineering.

cs.SE↗

arXiv2025

Predictive Analytics for Collaborators Answers, Code Quality, and Dropout on Stack Overflow

Previous studies that used data from Stack Overflow to develop predictive models often employed limited benchmarks of 3-5 models or adopted arbitrary selection methods. Despite being insightful, their limited scope suggests the need to benchmark more models to avoid overlooking untested algorithms. Our study evaluates 21 algorithms across three tasks: predicting the number of question a user is likely to answer, their code quality violations, and their dropout status. We employed normalisation, standardisation, as well as logarithmic and power transformations paired with Bayesian hyperparameter optimisation and genetic algorithms. CodeBERT, a pre-trained language model for both natural and programming languages, was fine-tuned to classify user dropout given their posts (questions and answers) and code snippets. We found Bagging ensemble models combined with standardisation achieved the highest R2 value (0.821) in predicting user answers. The Stochastic Gradient Descent regressor, followed by Bagging and Epsilon Support Vector Machine models, consistently demonstrated superior performance to other benchmarked algorithms in predicting user code quality across multiple quality dimensions and languages. Extreme Gradient Boosting paired with log-transformation exhibited the highest F1-score (0.825) in predicting user dropout. CodeBERT was able to classify user dropout with a final F1-score of 0.809, validating the performance of Extreme Gradient Boosting that was solely based on numerical data. Overall, our benchmarking of 21 algorithms provides multiple insights. Researchers can leverage findings regarding the most suitable models for specific target variables, and practitioners can utilise the identified optimal hyperparameters to reduce the initial search space during their own hyperparameter tuning processes.

cs.SE↗