Differential testing is a useful approach that uses different implementations of the same algorithms and compares the results for software testing. In recent years, this approach was successfully used for test campaigns of deep learning frameworks. There is little knowledge about the application of differential testing beyond deep learning. Within this article, we want to close this gap for classification algorithms. We conduct a case study using Scikit-learn, Weka, Spark MLlib, and Caret in which we identify the potential of differential testing by considering which algorithms are available in multiple frameworks, the feasibility by identifying pairs of algorithms that should exhibit the same behavior, and the effectiveness by executing tests for the identified pairs and analyzing the deviations. While we found a large potential for popular algorithms, the feasibility seems limited because, often, it is not possible to determine configurations that are the same in other frameworks. The execution of the feasible tests revealed that there is a large number of deviations for the scores and classes. Only a lenient approach based on statistical significance of classes does not lead to a huge amount of test failures. The potential of differential testing beyond deep learning seems limited for research into the quality of machine learning libraries. Practitioners may still use the approach if they have deep knowledge about implementations, especially if a coarse oracle that only considers significant differences of classes is sufficient.

Context The identification of bugs within issues reported to an issue tracking system is crucial for triage. Machine learning models have shown promising results for this task. However, we have only limited knowledge of how such models identify bugs. Explainable AI methods like LIME and SHAP can be used to increase this knowledge. Objective We want to understand if explainable AI provides explanations that are reasonable to us as humans and align with our assumptions about the model’s decision-making. We also want to know if the quality of predictions is correlated with the quality of explanations. Methods We conduct a study where we rate LIME and SHAP explanations based on their quality of explaining the outcome of an issue type prediction model. For this, we rate the quality of the explanations, i.e., if they align with our expectations and help us understand the underlying machine learning model. Results We found that both LIME and SHAP give reasonable explanations and that correct predictions are well explained. Further, we found that SHAP outperforms LIME due to a lower ambiguity and a higher contextuality that can be attributed to the ability of the deep SHAP variant to capture sentence fragments. Conclusion We conclude that the model finds explainable signals for both bugs and non-bugs. Also, we recommend that research dealing with the quality of explanations for classification tasks reports and investigates rater agreement, since the rating of explanations is highly subjective.