Perception and practices of differential testing.

Tens of thousands engineers are contributing to Google's codebase that spans billions of lines of code. To ensure high code quality, tremendous amount of effort has been made with new testing techniques and frameworks. However, with increasingly complex data structures and software systems, traditional test case based testing strategies cannot scale well to achieve the desired level of test adequacy. Differential (Diff) is one of the new testing techniques adapted to fill this gap. It uses the same input to run two versions of a software system, namely base and test, where base is the verified/tested version of the system while test is the modified version. The output of two runs are then thoroughly compared to find abnormalities that may lead to possible bugs. Over the past few years, differential testing has been quickly adopted by hundreds of teams across all major product areas at Google. Meanwhile, many new differential testing frameworks were developed to simplify the creation, maintenance, and analysis of diff tests. Curious by this emerging popularity, we conducted the first empirical study on differential testing in practice at large scale. In this study, we investigated common practices and usage of diff tests. We further explore the features of diff tests that users value the most and the pain points of using diff tests. Through this user study, we discovered that differential testing does not replace fine-grained testing techniques such as unit tests. Instead it supplements existing testing suites. It helps users verify the impact on unmodified and unfamiliar components in the absence of a test oracle. In terms of limitations, diff tests often take long time to run and appear to generate noisy and flaky outcomes. Finally, we highlight problems (including smart data differencing, sampling, and traceability) to guide future research in differential testing.