The API economy refers to the widespread integration of API (advanced programming interface) microservices, where software applications can communicate with each other, as a crucial element in business models and functions. The number of possible ways in which such a system could be used is huge. It is thus desirable to monitor the usage patterns and identify when the system is used in a way that was never used before. This provides a warning to the system analysts and they can ensure uninterrupted operation of the system. In this work we analyze both histograms and call graph of API usage to determine if the usage patterns of the system has shifted. We compare the application of nonparametric statistical and Bayesian sequential analysis to the problem. This is done in a way that overcomes the issue of repeated statistical tests and insures statistical significance of the alerts. The technique was simulated and tested and proven effective in detecting the drift in various scenarios. We also mention modifications to the technique to decrease its memory so that it can respond more quickly when the distribution drift occurs at a delay from when monitoring begins.

翻译：API经济是指将API(高级编程界面)微服务作为业务模式和功能中的一个关键要素广泛整合,使软件应用程序能够相互交流,这是业务模式和功能中的一个关键要素。这种系统可以使用的方法数量巨大,因此,有必要监测使用模式并确定系统何时被使用,这为系统分析员提供了警告,他们可以确保系统的不间断运作。在这项工作中,我们分析系统使用的直方图和调用图,以确定系统的使用模式是否已经发生变化。我们将非对称统计和巴耶斯相继分析的应用与问题进行比较。这样做的方式克服了反复统计测试的问题,确保了警报的统计重要性。该技术经过模拟和测试,并证明在发现各种情况中的漂移方面行之有效。我们还提到对技术的修改,以减少记忆,以便当分布流从监测开始时推迟时能够更快地作出反应。