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Test effectiveness refers to the capability of a test suite in exposingfaults in software.  It is crucial to be aware of factors that influence this capability.  We aim at inferring the causal relationship between the two factors (i.e.,Cover/Exec) and the capability of a test suite to expose and discover faults insoftware. Cover refers to the number of distinct test cases covering thestatement and Exec equals the number of times a test suite executes astatement. We analyzed 459166 software faults from {12} Java programs. Bayesianstatistics along with the back-door criterion was exploited for the purpose ofcausal inference. Furthermore, we examined the common pitfall measuringassociation, the mixture of causal and noncausal relationships, instead ofcausal association. The results show that Cover is of more causal associationas against \textit{Exec}, and the causal association and noncausal one forthose variables are statistically different. Software developers could exploitthe results to design and write more effective test cases, which lead todiscovering more bugs hidden in software.

Using causal inference and Bayesian statistics to explain the capability of a test suite in exposing software faults