基于启发式遗传算法的模糊测试样本集优化方案

模糊测试作为当前最有效的漏洞挖掘方法，不仅比其他漏洞挖掘技术更能应对复杂的程序，而且可扩展性很强。在数据量相对较大的测试中，模糊测试输入样本集存在质量低、冗余性高和可用性弱等问题。因此，对模糊测试输入样本集进行研究，提出了启发式遗传算法，借助0-1矩阵，通过启发式遗传算法对样本的执行路径进行选取和压缩，从而获得优化后兼顾样本质量的样本集最小样本集合，进而加快模糊测试的效率。实验结果表明:在没有损失的情况下，样本集精简后模糊测试的时间比精简前降低了22%，压缩率相比传统方案提升约40%。 

Fuzzing testing sample set optimization scheme based on heuristic genetic algorithm

As the most effective method of vulnerability mining at present, fuzzy testing not only is more capable of dealing with complex programs than other vulnerability mining techniques, but also has strong scalability. In the fuzzy testing with a large number of data, the input sample set has the problems of low quality, high redundancy and weak availability. Therefore, we study the input sample set of fuzzy testing, and propose a heuristic genetic algorithm. With the help of the 0-1 matrix, the execution path of the sample is selected and compressed through the heuristic genetic algorithm, so as to obtain the smallest sample set that takes into account the sample quality after optimization, thereby speeding up the efficiency of fuzzy testing. The experimental results show that, without loss, the fuzzy testing time after the sample set is simplified is reduced by 22% compared with that before the sample set is simplified, and the compression rate is increased by about 40% compared with the traditional scheme.