References

  1. Drools Fusion: Complex Event Processor. http://www.jboss.org/drools/drools-fusion.html.
  2. Acceleo. http://www.eclipse.org/acceleo/.
  3. A. Bertolino, A. Calabrò, F. Lonetti, A. Di Marco & A. Sabetta (2011): Towards a Model-Driven Infrastructure for Runtime Monitoring. In: SERENE, LNCS 6968. Springer, pp. 130–144. Available at http://dx.doi.org/10.1007/978-3-642-24124-6_13.
  4. A. Bertolino, A. Calabrò, F. Lonetti & A. Sabetta (2011): GLIMPSE: a generic and flexible monitoring infrastructure. In: Proceedings of EWDC, pp. 73–78. Available at http://doi.acm.org/10.1145/1978582.1978598.
  5. A. Bertolino, A. Di Marco & F. Lonetti (2012): Complex Events Specification for Properties Validation. In: 8th International Conference QUATIC 2012. IEEE Computer Society, pp. 85–94. Available at http://dx.doi.org/10.1109/QUATIC.2012.25.
  6. G. Casale & G. Serazzi (2011): Quantitative system evaluation with Java modeling tools. In: WOSP/SIPEW Conference, pp. 449–454. Available at http://doi.acm.org/10.1145/1958746.1958813.
  7. Paul Clements, Felix Bachmann, Len Bass, David Garlan, James Ivers, Reed Little, Robert Nord & Judith Stafford (2003): Documenting Software Architectures: Views and Beyond. Addison-Wesley, Boston, MA.
  8. V. Cortellessa, A. Di Marco & P. Inverardi (2011): Model-Based Software Performance Analysis. Springer. Available at http://dx.doi.org/10.1007/978-3-642-13621-4.
  9. V. Cortellessa & R. Mirandola (2002): PRIMA-UML: a performance validation incremental methodology on early UML diagrams. Sci. Comput. Program. 44(1), pp. 101–129. Available at http://dx.doi.org/10.1016/S0167-6423(02)00033-3.
  10. Vittorio Cortellessa, Antinisca Di Marco & Catia Trubiani (2014): An approach for modeling and detecting software performance antipatterns based on first-order logics. Software and System Modeling 13(1), pp. 391–432. Available at http://dx.doi.org/10.1007/s10270-012-0246-z.
  11. Eclipse Platform, Eclipse Modeling Project: http://www.eclipse.org/modeling/.
  12. J. L. Hennessy & D. A. Patterson (2007): Computer Architecture, A Quantitative Approach, fourth edition. Elsevier.
  13. B. Jacob, R. Lanyon-Hogg, D. K. Nadgir & A. F. Yassin (2004): A Practical Guide to the IBM Autonomic Computing Toolkit. ibm.com/redbooks.
  14. A. Kavimandan & A. S. Gokhale (2009): Applying Model Transformations to Optimizing Real-Time QoS Configurations in DRE Systems. In: QoSA, pp. 18–35. Available at http://dx.doi.org/10.1007/978-3-642-02351-4_2.
  15. F. Khomh, M. Di Penta, Y. Guéhéneuc & G. Antoniol (2012): An exploratory study of the impact of antipatterns on class change- and fault-proneness. Empirical Software Engineering 17(3), pp. 243–275. Available at http://dx.doi.org/10.1007/s10664-011-9171-y.
  16. F. Khomh, S. Vaucher, Y. Guéhéneuc & H. A. Sahraoui (2011): BDTEX: A GQM-based Bayesian approach for the detection of antipatterns. Journal of Systems and Software 84(4), pp. 559–572. Available at http://dx.doi.org/10.1016/j.jss.2010.11.921.
  17. L. Kleinrock (1975): Queueing Systems Vol. 1:Theory. Wiley.
  18. A. Di Marco, C. Pompilio, A. Bertolino, A. Calabrò, F. Lonetti & A. Sabetta (2011): Yet another meta-model to specify non-functional properties. In: ACM Proceedings of QASBA 2011, pp. 9–16. Available at http://dx.doi.org/10.1145/2031746.2031751.
  19. A. Martens, H. Koziolek, S. Becker & R. Reussner (2010): Automatically improve software architecture models for performance, reliability, and cost using evolutionary algorithms. In: WOSP/SIPEW Conference, pp. 105–116. Available at http://dx.doi.org/10.1145/1712605.1712624.
  20. R. Mirandola & C. Trubiani (2012): A Deep Investigation for QoS-based Feedback at Design Time and Runtime. In: IEEE International Conference on Engineering of Complex Computer Systems, pp. 147–156. Available at http://doi.ieeecomputersociety.org/10.1109/ICECCS.2012.3.
  21. N. Moha, Y. Guéhéneuc, L. Duchien & A. Le Meur (2010): DECOR: A Method for the Specification and Detection of Code and Design Smells. IEEE Trans. Software Eng. 36(1), pp. 20–36. Available at http://dx.doi.org/10.1109/TSE.2009.50.
  22. N. Moha, F. Palma, M. Nayrolles, B. Joyen Conseil, Y. Guéhéneuc, B. Baudry & J. Jézéquel (2012): Specification and Detection of SOA Antipatterns. In: In ICSOC 2012, pp. 1–16. Available at http://dx.doi.org/10.1007/978-3-642-34321-6_1.
  23. Object Management Group (OMG) (2004): UML 2.0 Superstructure Specification.
  24. Object Management Group (OMG) (2009): UML Profile for MARTE.
  25. R. Oliveto, F. Khomh, G. Antoniol & Y. Guéhéneuc (2010): Numerical Signatures of Antipatterns: An Approach Based on B-Splines. In: Conference on Software Maintenance and Reengineering, pp. 248–251. Available at http://dx.doi.org/10.1109/CSMR.2010.47.
  26. T. Parsons & J. Murphy (2008): Detecting Performance Antipatterns in Component Based Enterprise Systems. Journal of Object Technology 7(3), pp. 55–91. Available at http://dx.doi.org/10.5381/jot.2008.7.3.a1.
  27. C. U. Smith & C. V. Millsap (2008): Software Performance Engineering for Oracle Applications: Measurements and Models. In: Int. Computer Measurement Group Conference, pp. 331–342.
  28. C. U. Smith & L. G. Williams (2003): More New Software Performance Antipatterns: Even More Ways to Shoot Yourself in the Foot. In: Computer Measurement Group Conference, pp. 717–725.
  29. G. Travassos, F. Shull, M. Fredericks & V. R. Basili (1999): Detecting defects in object-oriented designs: using reading techniques to increase software quality. In: ACM SIGPLAN Conference, pp. 47–56. Available at http://doi.acm.org/10.1145/320384.320389.
  30. C. M. Woodside, G. Franks & D. C. Petriu (2007): The Future of Software Performance Engineering. In: FOSE, pp. 171–187. Available at http://doi.acm.org/10.1145/1253532.1254717.
  31. Jing Xu (2012): Rule-based automatic software performance diagnosis and improvement. Perform. Eval. 69(11), pp. 525–550. Available at http://dx.doi.org/10.1016/j.peva.2009.11.003.
  32. T. Zheng & C. M. Woodside (2003): Heuristic Optimization of Scheduling and Allocation for Distributed Systems with Soft Deadlines. In: Computer Performance Evaluation / TOOLS, pp. 169–181. Available at http://dx.doi.org/10.1007/978-3-540-45232-4_11.

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