This work is concerned with analysing the potential impact of direct changes to large- scale enterprise systems, and, in particular, how to minimise testing efforts on such changes. A typical enterprise system may consist of hundreds of thousands of classes and millions of methods. Thus, it is extremely costly and difficult to apply conventional testing techniques to such a system. Retesting everything after a change is very expensive, and in practice generally not necessary. Selective testing can be more effective. However, it requires a deep understanding of the target system and a lack of that understanding can lead to insufficient test coverage. Change Impact Analysis can be used to estimate the impacts of the changes to be applied, providing developers/testers with confidence in selecting necessary tests and identifying untested entities. Conventional change impact analysis approaches include static analysis, dynamic analysis or a hybrid of the two analyses. They have proved to be useful on small or medium size programs, providing users an inside view of the system within an acceptable running time. However, when it comes to large-scale enterprise systems, the sizes of the programs are orders of magnitude larger. Conventional approaches often run into resource problems such as insufficient memory and/or unacceptable running time (up to weeks). More critically, a large number of false-negatives and false-positives can be generated from those approaches.In this work, a conservative static analysis with the capability of dealing with inheritance was conducted on an enterprise system and associated changes to obtain all the potential impacts. Later an aspect-based dynamic analysis was used to instrument the system and collect a set of dynamic impacts at run-time. We are careful not to discard impacts unless we can show that they are definitely not impacted by the change. Reachability analysis examines the program to see “Whether a given path in a program representation corresponds to a possible execution path”. In other words, we employ reachability analysis to eliminate infeasible paths (i.e., miss-matched calls and returns) that are identified in the control-flow of the program. Furthermore, in the phase of alias analysis, we aim at identifying paths that are feasible but cannot be affected by the direct changes to the system, by searching a set of possible pairs of accesses that may be aliased at each program point of interest. Our contributions are, we designed a hybrid approach that combines static anal- ysis and dynamic analysis with reachability analysis and alias/pointer analysis, it can be used to (1) solve the scalability problem on large-scale systems, (2) reduce false-positives and not introduce false-negatives, (3) extract both direct and indirect changes, and (4) identify impacts even before making the changes. Using our approach, organizations can focus on a much smaller, relevant subset of the overall test suite instead of blindly doing their entire suite of tests. Also it enables testers to augment the test suite with tests applying to uncovered impacts. We include an empirical study that illustrates the savings that can be attained. / Thesis / Doctor of Philosophy (PhD)