Collecting traces from software running in the field is both useful and challenging. Traces may indeed help revealing unexpected usage scenarios, detecting and reproducing failures, and building behavioral models that reflect how the software is actually used. On the other hand, recording traces is an intrusive activity that may annoy users, negatively affecting the usability of the applications, if not properly designed. In this paper we address field monitoring by introducing Controlled Burst Recording, a monitoring solution that can collect comprehensive runtime data without compromising the quality of the user experience. The technique encodes the knowledge extracted from the monitored application as a finite state model that both represents the sequences of operations that can be executed by the users and the corresponding internal computations that might be activated by each operation. Our initial assessment with information extracted from ArgoUML shows that Controlled Burst Recording can reconstruct behavioral information more effectively than competing sampling techniques, with a low impact on the system response time.

CBR: Controlled Burst Recording

Daniela Briola;Leonardo Mariani
2020-01-01

Abstract

Collecting traces from software running in the field is both useful and challenging. Traces may indeed help revealing unexpected usage scenarios, detecting and reproducing failures, and building behavioral models that reflect how the software is actually used. On the other hand, recording traces is an intrusive activity that may annoy users, negatively affecting the usability of the applications, if not properly designed. In this paper we address field monitoring by introducing Controlled Burst Recording, a monitoring solution that can collect comprehensive runtime data without compromising the quality of the user experience. The technique encodes the knowledge extracted from the monitored application as a finite state model that both represents the sequences of operations that can be executed by the users and the corresponding internal computations that might be activated by each operation. Our initial assessment with information extracted from ArgoUML shows that Controlled Burst Recording can reconstruct behavioral information more effectively than competing sampling techniques, with a low impact on the system response time.
2020
2020 IEEE 13th International Conference on Software Testing, Verification and Validation ICST 2020. Proceedings
978-1-7281-5777-1
IEEE International Conference on Software Testing, Verification and Validation (ICST) 2020
Porto, Portogallo
2020
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2118490
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact