Although the security testing of Web systems can be automated by generating crafted inputs, solutions to automate the test oracle, i.e., distinguishing correct from incorrect outputs, remain preliminary. Specifically, previous work has demonstrated the potential of metamorphic testing; indeed, security failures can be determined by metamorphic relations that turn valid inputs into malicious inputs. However, without further guidance, metamorphic relations are typically executed on a large set of inputs, which is time-consuming and thus makes metamorphic testing impractical. We propose AIM, an approach that automatically selects inputs to reduce testing costs while preserving vulnerability detection capabilities. AIM includes a clustering-based black box approach, to identify similar inputs based on their security properties. It also relies on a novel genetic algorithm able to efficiently select diverse inputs while minimizing their total cost. Further, it contains a problem-reduction component to reduce the search space and speed up the minimization process. We evaluated the effectiveness of AIM on two well-known Web systems, Jenkins and Joomla, with documented vulnerabilities. We compared AIM's results with four baselines. Overall, AIM reduced metamorphic testing time by 84% for Jenkins and 82% for Joomla, while preserving vulnerability detection. Furthermore, AIM outperformed all the considered baselines regarding vulnerability coverage.

翻译：尽管通过生成构造性输入可实现Web系统安全测试的自动化，但用于自动化测试预言（即区分正确与错误输出）的解决方案仍处于初步阶段。具体而言，先前研究已证明蜕变测试的潜力：通过将有效输入转化为恶意输入的蜕变关系，确实可以判定安全故障。然而，若缺乏进一步引导，蜕变关系通常需在大量输入集上执行，此过程耗时严重，导致蜕变测试难以实际应用。本文提出AIM方法，通过自动选择输入在保持漏洞检测能力的同时降低测试成本。AIM包含基于聚类的黑盒方法，可根据安全属性识别相似输入；同时采用新型遗传算法，能够高效选择多样化输入并最小化其总成本。此外，该方法还包含问题归约组件，以缩减搜索空间并加速最小化过程。我们在两个已知漏洞的Web系统（Jenkins与Joomla）上评估AIM的有效性，并与四种基线方法进行对比。总体而言，AIM在保持漏洞检测能力的前提下，将Jenkins的蜕变测试时间减少84%，Joomla减少82%。此外，在漏洞覆盖率方面AIM优于所有对比基线方法。