An Experience Report on Applying Passive Learning in a Large-Scale Payment Company

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Passive learning techniques infer graph models on the behavior of a system from large trace logs. The research community has been dedicating great effort in making passive learning techniques more scalable and ready to use by industry. However, there is still a lack of empirical knowledge on the usefulness and applicability of such techniques in large scale real systems. To that aim, we conducted action research over nine months in a large payment company. Throughout this period, we iteratively applied passive learning techniques with the goal of revealing useful information to the development team. In each iteration, we discussed the findings and challenges to the expert developer of the company, and we improved our tools accordingly. In this paper, we present evidence that passive learning can indeed support development teams, a set of lessons we learned during our experience, a proposed guide to facilitate its adoption, and current research challenges.
Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017
Place of PublicationLos Alamitos, CA
Number of pages10
ISBN (Electronic)78-1-5386-0992-7
Publication statusPublished - 2017
EventICSME 2017: 33rd International Conference on Software Maintenance and Evolution - Shanghai, China
Duration: 17 Sep 201724 Sep 2017
Conference number: 33


ConferenceICSME 2017
Abbreviated titleICSME
Internet address


  • passive learning
  • experience report
  • dfasat


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