Social coverage for customized test adequacy and selection criteria

Abstract

Test coverage information can be very useful for guiding testers in enhancing their test suites to exercise possible uncovered entities and in deciding when to stop testing. However, for complex applications that are reused in different contexts and for emerging paradigms (e.g., component-based development, service-oriented architecture, and cloud computing), traditional coverage metrics may no longer provide meaningful information to help testers on these tasks. Various proposals are advocating to leverage information that come from the testing community in a collaborative testing approach. In this work we introduce a coverage metric, the Social Coverage, that customizes coverage information in a given context based on coverage data collected from similar users. To evaluate the potential of our proposed approach, we instantiated the social coverage metric in the context of a real world service oriented application. In this exploratory study, we were able to predict the entities that would be of interest for a given user with an average precision of 97% and average recall of 75%. Our results suggest that, in similar environments, social coverage can provide a better support to testers than traditional coverage.