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Continuous integration (CI) has become a popular method for automating code changes, testing, and software project delivery. However, sufficient testing prior to code submission is crucial to prevent build breaks. Additionally, testing must provide developers with quick feedback on code changes, which requires fast testing times. While regression test selection (RTS) has been studied to improve the cost-effectiveness of regression testing for lower-level tests (i.e., unit tests), it has not been applied to the testing of user interfaces (UI) in application domains such as mobile apps. UI testing at the UI level requires different techniques such as impact analysis and automated test execution. In this paper, we examine the use of RTS in CI settings for UI testing across various open-source mobile apps. Our analysis focuses on using Frequency Analysis to understand the need for RTS, Cost Analysis to evaluate the cost of impact analysis and test case selection algorithms, and Test Reuse Analysis to determine the reusability of UI test sequences for automation. The insights from this study will guide practitioners and researchers in developing advanced RTS techniques that can be adapted to CI environments for mobile apps. 

Mocking frameworks provide convenient APIs, which create mock objects, manipulate their behavior, and verify their execution, for the purpose of isolating test dependencies in unit testing. This study contributes an in-depth empirical study of whether and how mocking frameworks are used in Apache projects. The key findings and insights of this study include: First, mocking frameworks are widely used in 66% of Apache Java projects, with Mockito, EasyMock, and PowerMock being the top three most popular frameworks. Larger-scale and more recent projects tend to observe a stronger need to use mocking frameworks. This underscores the importance of mocking in practice and related future research. Second, mocking is overall practiced quite selectively in software projects—not all test files use mocking, nor all dependencies of a test target are mocked. It calls for more future research to gain a more systematic understanding of when and what to mock to provide formal guidance to practitioners. On top of this, the intensity of mocking in different projects shows different trends in the projects’ evolution history—implying the compound effects of various factors, such as the pace of a project’s growth, the available resources, time pressure, and priority, etc. This points to an important future research direction in facilitating best mocking practices in software evolution. Furthermore, we revealed the most frequently used APIs in the three most popular frameworks, organized based on the function types. The top five APIs in each functional type of the three mocking frameworks usually take the majority (78% to 100%) of usage in Apache projects. This indicates that developers can focus on these APIs to quickly learn the common usage of these mocking frameworks. We further investigated informal methods of mocking, which do not rely on any mocking framework. These informal mocking methods point to potential sub-optimal mocking practices that could be improved, as well as limitations of existing mocking frameworks. Finally, we conducted a developer survey to collect additional insights regarding the above analysis based on their experience, which complements our analysis based on repository mining. Overall, this study offers practitioners profound empirical knowledge of how mocking frameworks are used in practice and sheds light on future research directions to enhancing mocking in practice. 

Unit testing focuses on verifying the functions of individual units of a software system. It is challenging due to the high inter dependencies among software units. Developers address this by mocking—replacing the dependency by a “fake” object. Despite the existence of powerful, dedicated mocking frameworks, developers often turn to a “hand-rolled” approach—inheritance. That is, they create a subclass of the dependent class and mock its behavior through method overriding. However, this requires tedious implementation and compromises the design quality of unit tests. This work contributes a fully automated refactoring framework to identify and replace the usage of inheritance by using Mockito—a well received mocking framework. Our approach is built upon the empirical experience from five open source projects that use inheritance for mocking. We evaluate our approach on nine other projects. Results show that our framework is efficient, generally applicable to new datasets, mostly preserves test case behaviors in detecting defects (in the form of mutants), and decouples test code from production code. The qualitative evaluation by experienced developers suggests that the auto-refactoring solutions generated by our framework improve the quality of the unit test cases in various aspects, such as making test conditions more explicit, as well as improved cohesion, readability, understandability, and maintainability with test cases. Finally, we submit 23 pull requests containing our refactoring solutions to the open-source projects. It turns out that, 9 requests are accepted/merged, 6 requests are rejected, the remaining requests are pending (5 requests), with unexpected exceptions (2 requests), or undecided (1 request). In particular, among the 21 open source developers that are involved in the reviewing process, 81% give positive votes. This indicates that our refactoring solutions are quite well received by the open-source projects and developers.