The constant modification of software during its life cycle poses many challenges for developers and testers because changes might not behave as expected or may introduce erroneous side effects. For those reasons, it is of critical importance to analyze, test, and validate software every time it changes. The most common method for validating modified software is regression testing, which identifies differences in the behavior of software caused by changes and determines the correctness of those differences. Most research to this date has focused on the efficiency of regression testing by selecting and prioritizing existing test cases affected by changes. However, little attention has been given to finding whether the test suite adequately tests the effects of changes (i.e., behavior differences in the modified software) and which of those effects are missed during testing. In practice, it is necessary to augment the test suite to exercise the untested effects. The thesis of this research is that the effects of changes on software behavior can be computed with enough precision to help testers analyze the consequences of changes and augment test suites effectively. To demonstrate this thesis, this dissertation uses novel insights to develop a fundamental understanding of how changes affect the behavior of software. Based on these foundations, the dissertation defines and studies new techniques that detect these effects in cost-effective ways. These techniques support test-suite augmentation by (1) identifying the effects of individual changes that should be tested, (2) identifying the combined effects of multiple changes that occur during testing, and (3) optimizing the computation of these effects.