Adaptive Testing Based on Moment Estimation
提出一种基于矩估计的自适应测试方法，降低计算开销而不影响测试效率，在西门子测试套件上验证了可行性。
Adaptive testing (AT) is a software testing approach that uses a feedback mechanism to enhance test effectiveness. Its testing strategy can be adjusted online by using the testing data collected during the software testing process. However, it requires complex parameter estimation which results in excessive computational overhead that may hinder the applicability of AT. In this paper, we propose an approach called AT based on moment estimation (AT-ME) to address this problem. The proposed approach uses moment estimation to serve as the algorithm of parameter estimation, which reduces the complexity of AT-ME. In addition, a dynamic length for testing action is set to limit the number of decisions without influencing the test effectiveness. The proposed approach has been validated on the Siemens test suite, which includes seven real programs. The experiments show that AT-ME can reduce the computational overhead of AT without compromising overall testing efficiency. Results demonstrate that AT-ME is a feasible and effective AT strategy.