How far are we from the definition of a common software performance ontology?

ABSTRACT

The recent approaches to software performance modeling and validation share the idea of annotating software models with information related to performance (e.g. operational profile) and transforming the annotated model into a performance model (e.g. a Stochastic Petri Net). Up to date, no standard has been defined to represent the information related to performance in software artifacts, although clear advantages in tool interoperability and model transformations would stem from it. This paper is aimed at questioning whether a software performance ontology (i.e. a standard set of concepts and relations) is achievable or not. We consider three meta-models defined for software performance, that are the Schedulability, Performance and Time profile of UML, the Core Scenario Model and the Software Performance Engineering meta-model. We devise two approaches to the creation of an ontology: (i) bottom-up, that extracts common knowledge from the meta-models, (ii) top-down, that is driven from a set of requirements.

REFERENCES

• 1: Simonetta Balsamo , Antinisca Di Marco , Paola Inverardi , Marta Simeoni, Model-Based Performance Prediction in Software Development: A Survey, IEEE Transactions on Software Engineering, v.30 n.5, p.295-310, May 2004 [doi>10.1109/TSE.2004.9]
• 2: Vittorio Cortellessa , Raffaela Mirandola, PRIMA-UML: a performance validation incremental methodology on early UML diagrams, Science of Computer Programming, v.44 n.1, p.101-129, July 2002 [doi>10.1016/S0167-6423(02)00033-3 ]
• 3: Greg Franks , Alex Hubbard , Shikharesh Majumdar , John Neilson , Dorina Petriu , Jerome Rolia , Murray Woodside, A toolset for performance engineering and software design of client-server systems, Performance Evaluation, v.24 n.1-2, p.117-136, Nov. 1995 [doi>10.1016/0166-5316(95)96869-T ]
• 4: G. Haring, C. Juiz, C. Kurz, R. Puigjaner, J. Zottl, Framework for the Performance Assessment of Architectural Options on Intelligent Distributed Applications, Proc. of Performance Metrics for Intelligent Systems Workshop, (2004).
• 5: H. Harreld, NASA Delays Satellite Launch After Finding Bugs in Software Program, Federal Computer Week, (1998).
• 6: Juan Pablo López-Grao , José Merseguer , Javier Campos, From UML activity diagrams to Stochastic Petri nets: application to software performance engineering, Proceedings of the 4th international workshop on Software and performance, January 14-16, 2004, Redwood Shores, California
• 7: Dorin C. Petriu , C. Murray Woodside, Software Performance Models from System Scenarios in Use Case Maps, Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools, p.141-158, April 14-17, 2002
• 8: D. B. Petriu, M. Woodside, A Metamodel for Generating Performance Models from UML Designs, Proc. of UML Conference, LNCS 3273, pp. 41--53, (2004).
• 9: Dorina C. Petriu , Hui Shen, Applying the UML Performance Profile: Graph Grammar-Based Derivation of LQN Models from UML Specifications, Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools, p.159-177, April 14-17, 2002
• 10: J. A. Rolia , K. C. Sevcik, The Method of Layers, IEEE Transactions on Software Engineering, v.21 n.8, p.689-700, August 1995 [doi>10.1109/32.403785 ]
• 11: Connie U. Smith, Performance Engineering of Software Systems, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1990
• 12: Connie U. Smith , Catalina M. Llado, Performance Model Interchange Format (PMIF 2.0): XML Definition and Implementation, Proceedings of the The Quantitative Evaluation of Systems, First International Conference on (QEST'04), p.38-47, September 27-30, 2004 [doi>10.1109/QEST.2004.34]
• 13: C. U. Smith , L. G. Williams, A performance model interchange format, Journal of Systems and Software, v.49 n.1, p.63-80, Dec. 15, 1999 [doi>10.1016/S0164-1212(99)00067-9 ]
• 14: Connie U. Smith , Lloyd G. Williams, Software performance antipatterns, Proceedings of the 2nd international workshop on Software and performance, p.127-136, September 2000, Ottawa, Ontario, Canada [doi>10.1145/350391.350420]
• 15: Performance solutions: a practical guide to creating responsive, scalable software, Addison Wesley Longman Publishing Co., Inc., Redwood City, CA, 2002
• 16: H. L. Truong, T. Fahringer, Performance Analysis, Data Sharing and Tools Integration in Grids: New Approach based on Ontology, Proc. of International Conference on Computational Science, LNCS 3038, (2004).
• 17: Lloyd G. Williams , Connie U. Smith, Information Requirements for Software Performance Engineering, Proceedings of the 8th International Conference on Modelling Techniques and Tools for Computer Performance Evaluation: Quantitative Evaluation of Computing and Communication Systems, p.86-101, September 20-22, 1995
• 18: Webster's Third New International Dictionary.
• 19: Meta-Object Facility, OMG Final Adopted Specification, ptc/03-10-04 (2003).
• 20: UML Profile for QoS and Fault Tolerance, OMG Final Adopted Specification, ptc/04-09-01 (2004).
• 21: MOF 2.0 Query/Views/Transformations, OMG Document, ad/04-04-01 (2004).
• 22: UML Profile for Schedulability, Performance and Time, OMG Full Specification, formal/03-09-01 (2003).
• 23: ACM Proceedings of Workshop on Software and Performance (1998-2004).
• 24: www.perfeng.com/index.htm
• 25: www.sce.carleton.ca/rads/puma/

INDEX TERMS

Keywords:
meta-modeling, performance modeling, performance ontology, unified modeling language