基于最优路径的测试选择策略的多算法实现
Employment of Multiple Algorithms for Optimal Path-based Test Selection Strategy
摘要：
在被测系统中执行各种系统功能序列是软件测试的主要技术之一。创建有效的、一致的和高效的测试序列的自然方法是对被测系统进行建模，并采用一种算法来生成满足定义的测试覆盖标准的测试。测试集最优性的几个准则可以被定义。此外，为了从经济的角度优化测试集，必须定义被测系统模型各个部分的优先级。使用这种优先级，测试用例会比那些低优先级的部分更强烈地执行被测系统的高优先级部分。从文献和我们的观察证据证实，找到一个通用的算法，产生一个最佳的测试集的所有测试覆盖率和测试集的最优性标准是一个具有挑战性的任务。此外，对于不同的个体问题实例，不同的算法提供最优结果。在本文中，我们提出了一个基于路径的策略来执行最佳的测试选择。该策略首先采用一组现有的算法生成测试集，然后，它评估每个测试集的最优性所选择的标准，最后，选择最佳的测试集。实验结果证实了该策略的有效性和实用性。对于50个被测系统模型的各个实例，不同的算法提供了最佳结果;这些结果因所需的测试覆盖水平、模型优先级部分的大小以及所选测试集最优性标准而异。
英文摘要：
Executing various sequences of system functions in a system under test represents one of the primary techniques in software testing. The natural way to create effective, consistent and efficient test sequences is to model the system under test and employ an algorithm to generate the tests that satisfy a defined test coverage criterion. Several criteria of test set optimality can be defined. In addition, to optimize the test set from an economic viewpoint, the priorities of the various parts of the system model under test must be defined. Using this prioritization, the test cases exercise the high priority parts of the system under test more intensely than those with low priority. Evidence from the literature and our observations confirm that finding a universal algorithm that produces an optimal test set for all test coverage and test set optimality criteria is a challenging task. Moreover, for different individual problem instances, different algorithms provide optimal results. In this paper, we present a path-based strategy to perform optimal test selection. The strategy first employs a set of current algorithms to generate test sets; then, it assesses the optimality of each test set by the selected criteria, and finally, chooses the optimal test set. The experimental results confirm the validity and usefulness of this strategy. For individual instances of 50 system under test models, different algorithms provided optimal results; these results varied by the required test coverage level, the size of the priority parts of the model, and the selected test set optimality criteria.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
