基于实验的自适应随机测试效率分析

自适应随机测试通过自适应算法对随机测试进行加强，从而提高软件失效检测能力.现有研究过多强调了其在F-度量上的优势，而较少考虑输入域的诸多因素对自适应随机测试效率的影响.选取3类典型自适应随机测试算法和随机测试算法，分析被测软件失效区域紧致程度、维度对算法的影响，并选取自适应随机算法中测试效果最佳的固定候选集算法在非数值条件下与随机算法比对.结果表明，自适应随机测试受输入域的诸多因素影响，适用性有限，实际测试中对测试效率的提升效果并不明显. 

Analysis of adaptive random testing efficiency based on the experiment

Adaptive random testing (ART) had been proposed as an enhancement of random testing with some algorithms, thus the ability of failure detection was improved. However, previous studies emphasised on the advantages of F-measure a lot but rarely considered the real testing environment. Three typical adaptive random testing algorithms and random testing algorithm were selected, and the impact of the compactness of failure regions, dimensions on the efficiency of ART was analyzed. Then fixed size candidate set (FSCS), which was the best ART algorithm, was compared with random testing in the non-numeric conditions. The results show that the range of application of ART is limited by many factors of input domain. And in real test, the enhancement of test efficiency is not obvious.