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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.

Procedings 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). 2017.

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 Procedings 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). 33rd International Conference on Software Maintenance and Evolution, Shanghai, China, 17 September.

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 Procedings 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME).

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 Procedings 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). 2017.

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.

Procedings 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). 2017.

Research output: Scientific - peer-reviewConference contribution

BibTeX

@inbook{b463c54ad69f4db49fcccbeb6e2ddf09,
title = "An Experience Report on Applying Passive Learning in a Large-Scale Payment Company",
author = "Rick Wieman and {Finavaro Aniche}, Mauricio and Willem Lobbezoo and Sicco Verwer and {van Deursen}, Arie",
year = "2017",
booktitle = "Procedings 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)",

}

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.

M3 - Conference contribution

BT - Procedings 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)

ER -

ID: 23335488