Predicting and monitoring payment volumes with Spark and ElasticSearch
As a Masters student studying Embedded Systems at Delft University of Technology, I was very interested in automated testing. At one of the university’s recruitment days, most recruiters didn’t really know what Embedded Systems are, until by chance I met an Adyen representative who told me that the company was using robots to automate the testing of their point-of-sale system. These robots simulate the actions normally done by a real person, like inserting a card, typing in PIN code, and so on. My interest was immediately sparked.
In mid-2016 I was excited to join Adyen as an intern on the development team. From day one, interns are expected to solve real problems for the company, and I was no exception. In my case, I had the opportunity to improve Adyen’s point-of-sale (POS) test setup by looking at data using passive learning.
From a shopper’s perspective, doing an in-store payment seems to be fairly simple. However, the configurations behind a point-of-sale payment are actually quite complex. Some of the possible variations to consider are:
In addition, there are multiple other scenarios to consider. For example, the shopper may abort the transaction (at any stage), remove their card, and so on. In all, there are hundreds of possible payment flows.
While automation software such as Jenkins can run hundreds of tests for Adyen’s backend code within seconds, the robots to test the transactions need much longer. With the mushrooming numbers of payment configurations, it was not feasible to run all our tests in this manner, and so we began to investigate new ways to scale how to test our solution and analyze the performance.
I investigated possibilities to use the log files produced by the payment terminals, to see if the terminals in the field perform as we expect, and whether what the robots are testing actually covers the main flows in the field. The approach that sparked our interest for this was passive learning.
Passive learning tools try to describe the behavior of a system by transforming observations into a model. The observations in our case are log files, and the model is a graph model. In this model, it is possible to see which flows occur the most frequently, where flows split (for example: PIN versus signature), what happens to transactions that do not get approved, and so on.
DFASAT — a tool with greedy merging algorithm, based on Blue-Fringe, which takes traces (fixed format) as input, which contain the different events as well as the trace type (i.e., accepting or rejecting), and outputs an edge-labeled automaton.
Passive learning tools are generally being used on synthetic examples from academia, or for (reverse engineering) competitions like STAMINA. We couldn’t find any evidence of real industrial usages of those tools, and it turns out that applying them to a real system is actually challenging.
Under the direction of my supervisor and with an Adyen colleague, over a nine-month period I improved the output produced by the tools, by following an iterative evaluation process. In each iteration, we discussed possible improvements to the use of passive learning at the company with the expert developer, worked on the improvements, and analyzed the new results.
In particular, after a few experiments, we chose DFASAT as the tool to customize. All our changes and improvements are available as open source on our fork on Bitbucket. We adjusted one of the existing heuristics for our payment domain. For example, we introduced colors and numbers corresponding to different end states (Approved, Cancelled, Declined, Error), and we incorporated timestamp differences between log lines to see the bottlenecks in our system.
We learned about potential bottlenecks in the system, differences between card brands, bugs in upcoming releases, and much more. Some of the key things we found included:
Identifying potential bottlenecks and issues had a real impact on improving Adyen’s POS system, and a flow on effect for its POS customers, which include some of the major luxury and retail brands in the world.
One of the robot setups
Another great outcome of my research at Adyen was the possibility to present my findings at the International Conference on Software Maintenance and Evolution (ICSME) in Shanghai. The slides for this are available on SlideShare.
Moreover, after graduating, I joined Adyen’s point-of-sale quality assurance team where we are still using (DFASAT/flexfringe) to analyze and improve our point-of-sale solution. Also, as the robots have been so valuable in our testing process, we recently added another 13 to increase our testing capacity.
Arie van Deursen, Professor in Software Engineering at Delft University and supervisor on this project, recently presented a keynote at the Automated Software Engineering Conference in Chicago on the topic Software Engineering without Borders, including discussion on Rick’s research. The video is now available on Youtube, with the most relevant part from 23:15 –27:10.
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