Automatic generation of object oriented unit tests using genetic programming

Automating the generation of object-oriented unit tests for structural testing techniques has been challenging many researchers due to the benefits it promises in terms of cost saving and test quality improvement. It requires test sequences to be generated, each of which models a particular scenario in which the class under test is examined. The generation process aims at obtaining a preferably compact set of test sequences which attains a high degree of structural coverage. The degree of achieved structural coverage indicates the adequacy of the tests and hence the test quality in general. Existing approaches to automatic test generation for object-oriented software mainly rely either on symbolic execution and constraint solving, or on a particular search technique. However, these approaches suffer from various limitations which negatively affect both their applicability in terms of classes for which they are feasible, and their effectiveness in terms of achievable structural coverage. The approaches based on symbolic execution and constraint solving inherit the limitations of these techniques, which are, for instance, issues with scalability and problems with loops, arrays, and complex predicates. The search-based approaches encounter problems in the presence of complex predicates and complex method call dependences. In addition, existing work addresses neither testing non-public methods without breaking data encapsulation, nor the occurrence of runtime exceptions during test generation. Yet, data encapsulation, non-public methods, and exception handling are fundamental concepts of object-oriented software and require also particular consideration for testing. This thesis proposes a new approach to automating the generation of object-oriented unit tests. It employs genetic programming, a recent meta-heuristic optimization technique, which allows formulating the task of test sequence generation as a search problem more suitably than the search techniques applied by the existing approaches. The approach enables testing non-public methods and accounts for runtime exceptions by appropriately designing the objective functions that are used to guide the genetic programming search. The value of the approach is shown by a case study with real-world classes that involve non-public methods and runtime exceptions. The structural coverage achieved by the approach is contrasted with that achieved by a random approach and two commercial test sequence generators. In most of the cases, the approach of this thesis outperformed the other methods.