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Many approaches for testing configurable software systems start from the sameassumption: it is impossible to test all configurations. This motivated thedefinition of variability-aware abstractions and sampling techniques to copewith large configuration spaces. Yet, there is no theoretical barrier thatprevents the exhaustive testing of all configurations by simply enumeratingthem, if the effort required to do so remains acceptable. Not only this: webelieve there is lots to be learned by systematically and exhaustively testinga configurable system. In this case study, we report on the first everendeavour to test all possible configurations of an industry-strength, opensource configurable software system, JHipster, a popular code generator for webapplications. We built a testing scaffold for the 26,000+ configurations ofJHipster using a cluster of 80 machines during 4 nights for a total of 4,376hours (182 days) CPU time. We find that 35.70% configurations fail and weidentify the feature interactions that cause the errors. We show that samplingstrategies (like dissimilarity and 2-wise): (1) are more effective to findfaults than the 12 default configurations used in the JHipster continuousintegration; (2) can be too costly and exceed the available testing budget. Wecross this quantitative analysis with the qualitative assessment of JHipster'slead developers.

Test them all, is it worth it? Assessing configuration sampling on the JHipster Web development stack