Performance Model eXtractor
The manual creation of architectural performance models is very complex, time intense and error prone. The Performance Model eXtractor (PMX) tool automates the extraction of architectural performance models form measurement data. Currently, PMX supports logs of the Kieker Monitoring Framework as input data format. PMX separates the learning of generic aspects from model creation and is able to extract models of different formalisms. Currently there are builder implementations for Palladio Component Model and Descartes Modeling Language. More information can be found on the following pages:
- Webservices for PCM and DML (new)
- Download (eclipse update site and standalone archive)
- source code
- jenkins (currently only accessible within network of the university of wuerzburg)
If you have any questions, please contact Jürgen Walter.
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Automation in Software Performance Engineering Based on a Declarative Specification of Concerns. J. C. Walter; (2019).
Tools for Declarative Performance Engineering. J. Walter; S. Eismann; J. Grohmann; D. Okanovic; S. Kounev; in Companion of the 2018 ACM/SPEC International Conference on Performance Engineering (2018). 53–56.
Providing Model-Extraction-as-a-Service for Architectural Performance Models. J. Walter; S. Eismann; N. Reed; S. Kounev; in Proceedings of the 2017 Symposium on Software Performance (SSP) (2017).
CASPA: A Platform for Comparability of Architecture-based Software Performance Engineering Approaches. T. F. Düllmann; R. Heinrich; A. van Hoorn; T. Pitakrat; J. Walter; F. Willnecker; in Proceedings of the 2017 IEEE International Conference on Software Architecture (ICSA 2017) (2017).
An Expandable Extraction Framework for Architectural Performance Models. J. Walter; C. Stier; H. Koziolek; S. Kounev; in Proceedings of the 3rd International Workshop on Quality-Aware DevOps (QUDOS’17) (2017).
Mapping of Service Level Objectives to Performance Queries. J. Walter; D. Okanovic; S. Kounev; in Proceedings of the 2017 Workshop on Challenges in Performance Methods for Software Development (WOSP-C’17) co-located with 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) (2017).
Online Learning of Run-time Models for Performance and Resource Management in Data Centers. J. Walter; A. D. Marco; S. Spinner; P. Inverardi; S. Kounev; in Self-Aware Computing Systems, S. Kounev, J. O. Kephart, A. Milenkoski, X. Zhu (Hrsg.) (2017).
Automation and Simplification Through Declarative Performance Engineering. J. Walter; (2016, Juni).
Leistung von IT-Systemen vorhersagen. R. Emmerich; (2015, Dezember). 1–2.