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An Experience Report on Applying Passive Learning in a Large-Scale Payment Company. / Wieman, Rick; Finavaro Aniche, Mauricio; Lobbezoo, Willem; Verwer, Sicco; van Deursen, Arie.

Proceedings - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017. Los Alamitos, CA : IEEE Computer Society, 2017. p. 564-573.

Research output: Scientific - peer-reviewConference contribution

Harvard

Wieman, R, Finavaro Aniche, M, Lobbezoo, W, Verwer, S & van Deursen, A 2017, An Experience Report on Applying Passive Learning in a Large-Scale Payment Company. in Proceedings - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017. IEEE Computer Society, Los Alamitos, CA, pp. 564-573, ICSME 2017, Shanghai, China, 17/09/17. DOI: 10.1109/ICSME.2017.71

APA

Wieman, R., Finavaro Aniche, M., Lobbezoo, W., Verwer, S., & van Deursen, A. (2017). An Experience Report on Applying Passive Learning in a Large-Scale Payment Company. In Proceedings - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017 (pp. 564-573). Los Alamitos, CA: IEEE Computer Society. DOI: 10.1109/ICSME.2017.71

Vancouver

Wieman R, Finavaro Aniche M, Lobbezoo W, Verwer S, van Deursen A. An Experience Report on Applying Passive Learning in a Large-Scale Payment Company. In Proceedings - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017. Los Alamitos, CA: IEEE Computer Society. 2017. p. 564-573. Available from, DOI: 10.1109/ICSME.2017.71

Author

Wieman, Rick ; Finavaro Aniche, Mauricio ; Lobbezoo, Willem ; Verwer, Sicco ; van Deursen, Arie. / An Experience Report on Applying Passive Learning in a Large-Scale Payment Company. Proceedings - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017. Los Alamitos, CA : IEEE Computer Society, 2017. pp. 564-573

BibTeX

@inbook{b463c54ad69f4db49fcccbeb6e2ddf09,
title = "An Experience Report on Applying Passive Learning in a Large-Scale Payment Company",
abstract = "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.",
keywords = "passive learning, experience report, dfasat",
author = "Rick Wieman and {Finavaro Aniche}, Mauricio and Willem Lobbezoo and Sicco Verwer and {van Deursen}, Arie",
year = "2017",
doi = "10.1109/ICSME.2017.71",
pages = "564--573",
booktitle = "Proceedings - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017",
publisher = "IEEE Computer Society",
address = "United States",

}

RIS

TY - CHAP

T1 - An Experience Report on Applying Passive Learning in a Large-Scale Payment Company

AU - Wieman,Rick

AU - Finavaro Aniche,Mauricio

AU - Lobbezoo,Willem

AU - Verwer,Sicco

AU - van Deursen,Arie

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - passive learning

KW - experience report

KW - dfasat

UR - http://resolver.tudelft.nl/uuid:b463c54a-d69f-4db4-9fcc-cbeb6e2ddf09

U2 - 10.1109/ICSME.2017.71

DO - 10.1109/ICSME.2017.71

M3 - Conference contribution

SP - 564

EP - 573

BT - Proceedings - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017

PB - IEEE Computer Society

ER -

ID: 23335488