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Mocking in the context of automated software tests allows testing programunits in isolation. Designing realistic interactions between a unit and itsenvironment, and understanding the expected impact of these interactions on thebehavior of the unit, are two key challenges that software testers face whendeveloping tests with mocks. In this paper, we propose to monitor anapplication in production to generate tests that mimic realistic executionscenarios through mocks. Our approach operates in three phases. First, weinstrument a set of target methods for which we want to generate tests, as wellas the methods that they invoke, which we refer to as mockable method calls.Second, in production, we collect data about the context in which targetmethods are invoked, as well as the parameters and the returned value for eachmockable method call. Third, offline, we analyze the production data togenerate test cases with realistic inputs and mock interactions. The approachis automated and implemented in an open-source tool called RICK. We evaluateour approach with 3 real-world, open-source Java applications. RICK monitorsthe invocation of 128 methods in production across the 3 applications andcaptures their behavior. Based on this captured data, RICK generates test casesthat include realistic initial states and test inputs, mocks, and stubs. Thethree kinds of mock-based oracles generated by RICK verify the actualinteractions between the method and its environment. All the generated testcases are executable, and 52.4% of them successfully mimic the completeexecution context of the methods observed in production. The mock-based oraclesare effective at detecting regressions within the target methods, complementingeach other in their fault-finding ability. We interview 5 developers from theindustry who confirm the relevance of using production observations to designmocks and stubs.

Mimicking Production Behavior with Generated Mocks