组合交互测试中多目标粒子群和多线程的约束处理
Handling Constraints in Combinatorial Interaction Testing in the presence of Multi Objective Particle Swarm and Multithreading
摘要：
背景：组合测试策略最近由于其不同的应用而受到了广泛的关注。在其简单的形式中，组合策略可以基于它们的相互作用（或组合）将系统的几个输入参数（配置）减少到一个小集合中。在实践中，软件系统的输入配置受到约束，特别是在高度可配置的系统的情况下。要在策略中实现这一功能，会给构造带来许多困难。虽然现在有很多组合交互测试策略，但很少有支持约束的。目的：本文提出了一种新的策略，在存在约束条件的情况下构造组合交互测试集。方法：给出了详细的设计和算法.为了克服多个判断标准的最优解，多目标粒子群优化和多线程。该战略及其相关的算法进行了广泛的评估，使用不同的基准和比较。结果：我们的结果是有前途的评价结果显示了每个算法的效率和性能的战略。基准测试的结果也表明，该策略可以有效地生成约束测试套件相比，国家的最先进的战略。结论：该策略为约束组合交互测试集的构建提供了一种新的思路。该战略可为今后的执行工作奠定新的有效基础。
英文摘要：
Context: Combinatorial testing strategies have lately received a lot of attention as a result of their diverse applications. In its simple form, a combinatorial strategy can reduce several input parameters (configurations) of a system into a small set based on their interaction (or combination). In practice, the input configurations of software systems are subjected to constraints, especially in case of highly configurable systems. To implement this feature within a strategy, many difficulties arise for construction. While there are many combinatorial interaction testing strategies nowadays, few of them support constraints. Objective: This paper presents a new strategy, to construct combinatorial interaction test suites in the presence of constraints. Method: The design and algorithms are provided in detail. To overcome the multi-judgment criteria for an optimal solution, the multi-objective particle swarm optimization and multithreading are used. The strategy and its associated algorithms are evaluated extensively using different benchmarks and comparisons. Results: Our results are promising as the evaluation results showed the efficiency and performance of each algorithm in the strategy. The benchmarking results also showed that the strategy can generate constrained test suites efficiently as compared to state-of-the-art strategies. Conclusion: The proposed strategy can form a new way for constructing of constrained combinatorial interaction test suites. The strategy can form a new and effective base for future implementations.
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。
