Successful software systems continuously change their requirements and thus code. When this happens, some existing tests get broken because they no longer reflect the intended behavior, and thus they need to be updated. Repairing broken tests can be time-consuming and difficult.

When developers change a program, regression tests can fail not only due to faults in the program but also due to out-of-date test code that does not reflect the desired behavior of the program. When this occurs, it is necessary to repair test code such that the tests pass. Repairing tests manually is difficult and time consuming. We recently developed ReAssert, a tool that can automatically repair broken unit tests, but only if they lack complex control flow or operations on expected values.

Developers often change software in ways that cause tests to fail. When this occurs, developers must determine whether failures are caused by errors in the code under test or in the test code itself. In the latter case, developers must repair failing tests or remove them from the test suite. Repairing tests is time consuming but beneficial, since removing tests reduces a test suite's ability to detect regressions. Fortunately, simple program transformations can repair many failing tests automatically. We present ReAssert, a novel technique and tool that suggests repairs to failing tests' code which cause the tests to pass. Examples include replacing literal values in tests, changing assertion methods, or replacing one assertion with several. If the developer chooses to apply the repairs, ReAssert modifies the code automatically. Our experiments show that ReAssert can repair many common test failures and that its suggested repairs correspond to developers' expectations.

Automatic white-box test generation is a challenging problem. Many existing tools rely on complex code analyses and heuristics. As a result, structural features of an input program may impact tool effectiveness in ways that tool users and designers may not expect or understand. We develop a technique that uses structural program metrics to predict the test coverage achieved by three automatic test generation tools. We use coverage and structural metrics extracted from 11 software projects to train several decision tree classifiers. Our experiments show that these classifiers can predict high or low coverage with success rates of 82% to 94%.

Refactorings are behavior-preserving program transformations that improve the design of a program. Refactoring engines are tools that automate the application of refactorings: first the user chooses a refactoring to apply, then the engine checks if the transformation is safe, and if so, transforms the program. Refactoring engines are a key component of modern IDEs, and programmers rely on them to perform refactorings. A bug in the refactoring engine can have severe consequences as it can erroneously change large bodies of source code.