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Evolutionary unit testing of object-oriented software using strongly-typed genetic programming

机译：使用强遗传编程的面向对象软件的进化单元测试

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Evolutionary algorithms have successfully been applied to software testing. Not only approaches that search for numeric test data for procedural test objects have been investigated, but also techniques for automatically generating test programs that represent object-oriented unit test cases. Compared to numeric test data, test programs optimized for object-oriented unit testing are more complex. Method call sequences that realize interesting test scenarios must be evolved. An arbitrary method call sequence is not necessarily feasible due to call dependences which exist among the methods that potentially appear in a method call sequence. The approach presented in this paper relies on a tree-based representation of method call sequences by which sequence feasibility is preserved throughout the entire search process. In contrast to other approaches in this area, neither repair of individuals nor penalty mechanisms are required. Strongly-typed genetic programming is employed to generate method call trees. In order to deal with runtime exceptions, we use an extended distance-based fitness function. We performed experiments with four test objects. The initial results are promising: high code coverages were achieved completely automatically for all of the test objects.
机译：进化算法已成功应用于软件测试。不仅研究了搜索用于程序测试对象的数字测试数据的方法，而且还用于自动生成代表面向对象单元测试用例的测试程序的技术。与数字测试数据相比，针对面向对象的单元测试优化的测试程序更复杂。方法呼叫序列必须进化实现有趣的测试方案。任意方法呼叫序列不一定是可行的，因为呼叫依赖性在可能以方法呼叫序列中出现的方法中存在。本文呈现的方法依赖于通过在整个搜索过程中保留序列可行性的方法呼叫序列的基于树的表示。与该领域的其他方法形成鲜明对比，因此个人也不需要维修或惩罚机制。使用强类型的遗传编程来生成方法呼叫树。为了处理运行时异常，我们使用扩展的基于距离的健身功能。我们用四个测试对象进行了实验。初始结果很有希望：为所有测试对象完全自动实现高码覆盖。

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