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Context: Differential testing is a useful approach that uses differentimplementations of the same algorithms and compares the results for softwaretesting. In recent years, this approach was successfully used for testcampaigns of deep learning frameworks.  Objective: There is little knowledge on the application of differentialtesting beyond deep learning. Within this article, we want to close this gapfor classification algorithms.  Method: We conduct a case study using Scikit-learn, Weka, Spark MLlib, andCaret in which we identify the potential of differential testing by consideringwhich algorithms are available in multiple frameworks, the feasibility byidentifying pairs of algorithms that should exhibit the same behavior, and theeffectiveness by executing tests for the identified pairs and analyzing thedeviations.  Results: While we found a large potential for popular algorithms, thefeasibility seems limited because often it is not possible to determineconfigurations that are the same in other frameworks. The execution of thefeasible tests revealed that there is a large amount of deviations for thescores and classes. Only a lenient approach based on statistical significanceof classes does not lead to a huge amount of test failures.  Conclusions: The potential of differential testing beyond deep learning seemslimited for research into the quality of machine learning libraries.Practitioners may still use the approach if they have deep knowledge aboutimplementations, especially if a coarse oracle that only considers significantdifferences of classes is sufficient.

Differential testing for machine learning: an analysis for classification algorithms beyond deep learning