Metrics for BPEL process context-independency analysis
- 192 Downloads
- 1 Citations
Abstract
BPEL processes are workflow-oriented composite services for service-oriented solutions. Rapidly changing environment and turbulent market conditions require flexible BPEL processes to adapt with several modifications during their life cycles. Such adaptability and flexibility require the low degree of dependency or coupling between a BPEL process and its surrounding environment. In fact, heavy coupling and context dependency with partners provoke several undesirable drawbacks such as poor understandability, inflexibility, inadaptability, and defects. This paper is to propose metrics at the design phase to measure BPEL process context independency. With the aid of these metrics, the architect could analyze and control the context independency of a BPEL process quantitatively. To validate the metrics, authors collected a data set consisting 70 BPEL processes and also gathered the expert’s rating of context independency through conducting a controlled experiment. The obtained results reveal that there exists a high statistical correlation between the proposed metrics and the expert’s judgment of context independency.
Keywords
BPEL process coupling measurement Service coupling metrics Composite service context-independency Service-oriented metrics SOA coupling metric Workflow metricsPreview
Unable to display preview. Download preview PDF.
References
- 1.Kammer PJ, Bolcer GA, Taylor RN, Hitomi AS, Bergman M (2000) Techniques for supporting dynamic and adaptive workflow. Comput Support Coop Work 9(3–4): 269–292CrossRefGoogle Scholar
- 2.Reichert M, Rinderle S (2006) On Design Principles for Realizing Adaptive Service Flows with BPEL. Proc. EMISA. GI Lecture Notes in Informatics, LNI P-95, pp. 133–146. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.88.2255&rep=rep1&type=pdf
- 3.Cardoso J (2007) Complexity analysis of BPEL web processes, software process improvement and practice. Wiley InterScience 12: 35–49Google Scholar
- 4.Pressman RS (2001) Software engineering, a practitioner’s approach, 5th edn. McGrawHill, New YorkGoogle Scholar
- 5.Juric MB, Mathew B, Sarang P (2006) Business process execution language for web services, 2nd edn. Packt Publishing, BirminghamGoogle Scholar
- 6.Perepletchikov M, Ryan C, Frampton K (2006) Towards the definition and validation of coupling metrics for predicting maintainability in service-oriented designs, OTM workshops 2006. LNCS. Springer, Berlin, vol 4277, pp 34–35Google Scholar
- 7.Vanderfeesten I, Reijers HA, van der Aalst WM (2008) Evaluating workflow process designs using cohesion and coupling metrics.. Comput Ind 59(5): 420–437 doi: 10.1016/j.compind.2007.12.007 CrossRefGoogle Scholar
- 8.Perepletchikov M., Ryan C, Frampton K, Tari Z (2007) Coupling metrics for predicting maintainability in service-oriented designs. In: Software engineering conference, ASWEC 2007, Melbourne, Australia, pp 329–340Google Scholar
- 9.Stevens W, Myers G, Constantine L (1974) Structured design. IBM Syst J 13(2): 115–139CrossRefGoogle Scholar
- 10.Sindhgatta R, Sengupta B, Ponnalagu K (2009) Measuring the quality of service oriented design, ICSOC-ServiceWave 2009, LNCS 5900. Springer, Berlin, pp , pp 485–499Google Scholar
- 11.Pautasso C, Wilde E (2009) Why is the web loosely coupled? A multi-faceted metric for service design. In: Proceedings of the 18th world wide web conference, Madrid, SpainGoogle Scholar
- 12.Xu T, Qian K, He X (2006) Service oriented dynamic decoupling metrics. In: Proceedings of the 2006 international conference on semantic web and web services (SWWS’06), Las Vegas, USA, June 26–29, 2006, pp 170–176Google Scholar
- 13.Reijers HA, Vanderfeesten ITP (2004) Cohesion and Coupling Metrics for Work-flow Process Design. In: Desel J, Pernici B, Weske M (eds) International conference on business process management (BPM 2004), vol 3080 of Lecture Notes in Computer Science. Springer, Berlin, pp 290–305Google Scholar
- 14.Reijers HA (2003) A cohesion metric for the definition of activities in a workflow process. In: Proceedings of the Eighth CAiSE/IFIP8.1 international workshop on evaluation of modeling methods in systems analysis and design 2003, pp 116–125Google Scholar
- 15.Vanderfeesten I, Cardoso J, Reijers HA (2007) A weighted coupling metric for business process models. In: The 19th international conference on advanced information systems engineering (CAiSE Forum), pp 11–15Google Scholar
- 16.Steghuis C (2006) Service granularity in SOA projects: a trade-off analysis. M.Sc. Thesis, Business Information Technology, University of Twente, http://essay.utwente.nl/57339/1/scriptie_Steghuis.pdf. Accessed 14 June 2010
- 17.Papazoglou M (2003) Service-oriented computing: concepts, characteristics and directions. In: Fourth international conference on web information systems engineering, IEEE, Roma, ItalyGoogle Scholar
- 18.Orton JD, Weick KE (1990) Loosely coupled systems: a reconceptualization. Acad Manage Rev 15(2): 203–223Google Scholar
- 19.Hurwitz J, Bloor R, Baroudi C, Kaufman M (2007) Service- oriented architecture for dummies. Wiley, LondonGoogle Scholar
- 20.Erl T (2005) Service-oriented architecture: concepts, technology, and design. Prentice Hall, Englewood CliffsGoogle Scholar
- 21.Fenton NE, Pfleeger SL (1998) Software metrics: a rigorous and practical approach, 2nd edn. PWS Course Technology Ptr, BostonGoogle Scholar
- 22.Papazoglou MP, HeuvelWJ van den (2006) Service oriented design and development methodology. Int J Web Eng Technol 2(4): 412–442. doi: 10.1504/IJWET.2006.010423 CrossRefGoogle Scholar
- 23.Oracle BPEL Process Manager Developer’s Guide 10g Release 2 (10.1.2); BPEL processes common interaction patterns; http://download.oracle.com/docs/cd/B14099_19/integrate.1012/b14448/interact.htm. Accessed 20 July 2010
- 24.Barros A, Dumas M, ter Hofsted, Service Interaction patterns; Joint initiative by SAP and Queensland University of Technology, co-funded by Queensland State Government. http://math.ut.ee/~dumas/ServiceInteractionPatterns/patterns.html. Accessed 20 July 2010
- 25.Eclipse BPEL Designer Plug-in: http://www.eclipse.org/bpel/. Accessed 26 July 2010
- 26.Bowen TP, Post JV, Tsai J, Presson PE, Schmidt RL (1983) Software Quality Measurement for Distributed Systems, Guidebook for Software Quality Measurement, RADC-TR-83-175, vol II, Final Technical Report, Rome Air Development Center, Air Force Systems Command, Griffis Air Force Base, NYGoogle Scholar
- 27.Briand LC, Morasca S, Basili VR (1996) Property-based software engineering measurement. IEEE Trans Softw Eng 22(1): 68–86CrossRefGoogle Scholar
- 28.Perepletchikov M, Ryan C (2010) A controlled experiment for evaluating the impact of coupling on the maintainability of service-oriented software, IEEE transactions on software engineering, 01 June 2010. IEEE Computer Society Digital Library doi: 10.1109/TSE.2010.61
- 29.Zelkowitz MV, Wallace DR (1998) Experimental models for validating technology. IEEE Comput 31(5): 23–31Google Scholar
- 30.Basili VR, Briand LC, Melo WL (1996) A validation of object- oriented design metrics as quality indicators. IEEE Trans Softw Eng 22(10): 751–761CrossRefGoogle Scholar
- 31.Perry DE, Porter AA, Votta LG (2000) Empirical studies of software engineering: a roadmap. In: Finkelstein A (ed) The future of software engineering. ACM Press, New York, ISBN 1-58113- 253-0Google Scholar
- 32.Mendonca MG, Basili VR (2000) Validation of an approach for improving existing measurement frameworks. IEEE Trans Softw Eng 26(6): 484–499CrossRefGoogle Scholar
- 33.Juristo N, Vegas S (2010) The role of non-exact replications in software engineering experiments. Empirical Softw Eng. doi: 10.1007/s10664-010-9141-9
- 34.Shaughnessy J, Zechmeister EB, Zechmeister JS (2005) Research methods in psychology, 7th edn. McGraw-Hill Humanities Social, New YorkGoogle Scholar
- 35.Siegel S, Castellan NJ Jr (1988) Nonparametric statistics for the behavioural sciences. McGrawHill, New YorkGoogle Scholar
- 36.Dyba T, Kampenes VB, Sjoberg DIK (2006) A systematic review of statistical power in software engineering experiments. Inf Softw Technol 48(8): 745–755CrossRefGoogle Scholar
- 37.Hays WL (1994) Statistics, 5th edn. Harcourt Brace, New YorkGoogle Scholar
- 38.Faul F, Erdfelder E, Lang A, Buchner A (2007) G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 39(2): 175–191CrossRefGoogle Scholar