基于约束组合的测试用例生成
Case generation by constraints combinatorial testing

系统输入参数常有多种约束条件，且约束之间相互关联。违背约束条件及约束之间的依赖关系，是软件中常见的缺陷。当参数数量多、输入空间大时，组合测试可在保证覆盖率的同时有效降低测试代价。该文针对约束及约束组合的故障检测问题，将约束覆盖作为测试充分性准则，提出约束条件的组合测试方法。以典型的在线交易平台会员注册服务为例，对比OA（orthogonal array）、IPO（in-parameter order）和OFOT（one factor one time）3种组合算法应用于约束组合时的性能表现。实验选取不同的故障模式和实验配置，对3种组合算法从生成时间、故障检测能力、用例规模等方面进行了比较。实验结果表明：OA算法生成时间短、用例规模小且相对稳定、故障检测能力中等，适合迭代优化。
Abstract：The inputs parameters to software systems usually have various constraints correlated to each other. However, the input often violates the required constraints and constraint correlations. When there are many parameters with a large input domain, combinatorial testing can be used to reduce the test cost while ensuring test coverage. This paper defines a test adequacy criterion for constraint coverage using constrained combinatorial testing to detect conflicts caused by violating the constraints and constraint combinations. The system was applied to a member registration service for an online payment platform. Three typical combinatorial algorithms, OA (orthogonal array), IPO (in parameter order) and OFOT (one factor one time), were compared in forward their speeds, defect detection efficiency, and test case size using different configurations and parameter settings. The results showed that the OA algorithm was fast, produced fewer, more stable test cases with a reasonable defect detection efficiency, so this algorithm is good for iterative optimizations.