What Does Passive Learning Bring To Adyen
Rob Wieman · Research Repository (Delft University of Technology) · 2017
Analyzing large numbers of log entries can be challenging, especially when there are many short log entries that each describe only one execution of the system, either successful or unsuccessful. How can one determine whether the system is working correctly, based on these logs? The logs that are of interest (e.g., log entries pointing towards some anomaly in the system) may be hidden between all the logs that are of less interest. Luckily, there are so-called passive learning tools that infer a (graph) model from such set of logs, which allows the user to oversee all paths that were taken in the system. In this thesis, we discuss the opportunities for passive learning for Adyen, a large-scale payment company. We compare three different open source passive learning tools (namely Synoptic, InvariMint, and DFASAT) in terms of runtime performance and output complexity, and show that all tools struggle with an increasing input size. We also share the results of a survey we conducted under developers to identify their perceptions, and for which purpose(s) they would use such tools. Furthermore, we provide six examples of different types of analyses that are possible with passive learning (such as finding bugs, comparing within a context, and analyzing timings), and that are useful for the company. We include a short guide on how to adopt passive learning, and what we had to change in one of the tools to make it so useful. Finally, we show how a graph difference tool can help to compare different graphs, for example over different time intervals. This tool highlights differences in both structure and frequencies. Altogether, this shows what passive learning brings to Adyen.