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Tracing Back Log Data to its Log Statement: From Research to Practice. / Schipper, Daan; Aniche, Maurício; van Deursen, Arie.

Proceedings of the 16th International Conference on Mining Software Repositories. 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Schipper, D, Aniche, M & van Deursen, A 2019, Tracing Back Log Data to its Log Statement: From Research to Practice. in Proceedings of the 16th International Conference on Mining Software Repositories. 16th International Conference on Mining Software Repositories, Montreal, Canada, 26/05/19.

APA

Schipper, D., Aniche, M., & van Deursen, A. (2019). Tracing Back Log Data to its Log Statement: From Research to Practice. In Proceedings of the 16th International Conference on Mining Software Repositories

Vancouver

Schipper D, Aniche M, van Deursen A. Tracing Back Log Data to its Log Statement: From Research to Practice. In Proceedings of the 16th International Conference on Mining Software Repositories. 2019

Author

Schipper, Daan ; Aniche, Maurício ; van Deursen, Arie. / Tracing Back Log Data to its Log Statement: From Research to Practice. Proceedings of the 16th International Conference on Mining Software Repositories. 2019.

BibTeX

@inproceedings{9fc4a63c57bf4a80aca248f5a8fb08a3,
title = "Tracing Back Log Data to its Log Statement: From Research to Practice",
abstract = "Logs are widely used as a source of information to understand the activity of computer systems and to monitor their health and stability. However, most log analysis techniques require the link between the log messages in the raw log file and the log statements in the source code that produce them. Several solutions have been proposed to solve this non-trivial challenge, of which the approach based on static analysis reaches the highest accuracy. We, at Adyen, implemented the state-of-the-art research on log parsing in our logging environment and evaluated their accuracy and performance. Our results show that, with some adaptation, the current static analysis techniques are highly efficient and performant. In other words, ready for use.",
keywords = "software engineering, runtime monitoring, log parsing",
author = "Daan Schipper and Maur{\'i}cio Aniche and {van Deursen}, Arie",
year = "2019",
language = "English",
booktitle = "Proceedings of the 16th International Conference on Mining Software Repositories",

}

RIS

TY - GEN

T1 - Tracing Back Log Data to its Log Statement: From Research to Practice

AU - Schipper, Daan

AU - Aniche, Maurício

AU - van Deursen, Arie

PY - 2019

Y1 - 2019

N2 - Logs are widely used as a source of information to understand the activity of computer systems and to monitor their health and stability. However, most log analysis techniques require the link between the log messages in the raw log file and the log statements in the source code that produce them. Several solutions have been proposed to solve this non-trivial challenge, of which the approach based on static analysis reaches the highest accuracy. We, at Adyen, implemented the state-of-the-art research on log parsing in our logging environment and evaluated their accuracy and performance. Our results show that, with some adaptation, the current static analysis techniques are highly efficient and performant. In other words, ready for use.

AB - Logs are widely used as a source of information to understand the activity of computer systems and to monitor their health and stability. However, most log analysis techniques require the link between the log messages in the raw log file and the log statements in the source code that produce them. Several solutions have been proposed to solve this non-trivial challenge, of which the approach based on static analysis reaches the highest accuracy. We, at Adyen, implemented the state-of-the-art research on log parsing in our logging environment and evaluated their accuracy and performance. Our results show that, with some adaptation, the current static analysis techniques are highly efficient and performant. In other words, ready for use.

KW - software engineering

KW - runtime monitoring

KW - log parsing

M3 - Conference contribution

BT - Proceedings of the 16th International Conference on Mining Software Repositories

ER -

ID: 52060633