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Practitioners are increasingly dependent on publicly available resources forsupporting their knowledge needs during software development. This has thuscaused a spotlight to be paced on these resources, where researchers havereported mixed outcomes around the quality of these resources. Stack Overflow,in particular, has been studied extensively, with evidence showing that coderesources on this platform can be of poor quality at times. Limited researchhas explored the variables or factors that predict code quality on StackOverflow, but instead has focused on ranking content, identifying defects andpredicting future content. In many instances approaches used for prediction arenot evaluated to identify the best techniques. Contextualizing the StackOverflow code quality problem as regression-based, we examined the variablesthat predict Stack Overflow (Java) code quality, and the regression approachthat provides the best predictive power. Six approaches were considered in ourevaluation, where Gradient Boosting Machine (GBM) stood out. In addition,longer Stack Overflow code tended to have more code violations, questions thatwere scored higher also attracted more views and the more answers that areadded to questions on Stack Overflow the more errors were typically observed inthe code that was provided. Outcomes here point to the value of the GBMensemble learning mechanism, and the need for the practitioner community to beprudent when contributing and reusing Stack Overflow Java coding resource.

GBM Returns the Best Prediction Performance among Regression Approaches: A Case Study of Stack Overflow Code Quality