Abstract
BPM software automation projects require different approaches for effort estimation for they are developed based on business process models rather than traditional requirements analysis outputs. In this empirical research we examine the effect of various measures for BPMN compliant business process models on the effort spent to automate those models. Although different measures are suggested in the literature, only a few studies exist that relate these measures to effort estimation. We propose that different perspectives of business process models need to be considered such as behavioral, organizational, functional and informational to determine the automation effort effectively. The proposed measures include number of activities, number of participating roles, number of outputs from the process and control flow complexity. We examine the effect of these measures on the automation effort and propose a prediction model developed by multiple linear regression analysis. The data were collected from a large IS integration project which cost 300 person-months along a three-year time frame. The results indicate that some of the measures collected have significant effect on the effort spent to develop the BPM automation software. We envision that prediction models developed by using the suggested approach will be useful to make accurate estimates of project effort for BPM intensive software development projects.
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Cardoso, J., Mendling, J., Neumann, G., Reijers, H.A.: A Discourse on Complexity of Process Models. In: Eder, J., Dustdar, S. (eds.) BPM 2006 Workshops. LNCS, vol. 4103, pp. 117–128. Springer, Heidelberg (2006)
Coskuncay, A., Aysolmaz, B., Demirors, O., Bilen, O., Dogan, I.: Bridging The Gap Between Business Process Modeling And Software Requirements Analysis: A Case Study. In: MCIS 2010 Proceedings, paper 20 (2010), http://aisel.aisnet.org/mcis2010/20
COSMIC: The COSMIC Functional Size Measurement Method Version 3.0.1, Measurement Manual (The COSMIC Implementation Guide for ISO/IEC 19761: 2003). The Common Software Measurement International Consortium (COSMIC) (2009)
Curtis, B., Kellner, M., Over, J.: Process Modeling. Communications of the ACM, Special Issue on Analysis and Modeling in Software Development 35(9), 75–90 (1992), doi:10.1145/130994.130998
Myrtveit, I., Stensrud, E., Shepperd, M.: Reliability and Validity in Comparative Studies of Software Prediction Models. In. IEEE Transactions on Software Engineering 31(5), 380 (2005)
Dhammaraksa, K., Intakosum, S.: Measuring Size of Business Process From Use Case Descriptions. In: Computer Science and Information Technology, ICCSIT 2009, pp. 600–604. IEEE (2009)
Dijkman, R., Dumas, M., van Dongen, B., Kaarik, R., Mendling, J.: Similarity of Business Process Models: Metrics and Evaluation. Information Systems Journal 36, 498–516 (2011)
Ghani, A.A.A., Wei, K.T., Muketha, G.M., Wen, W.P.: Complexity Metrics for Measuring the Understandability and Maintainability of Business Process Models Using Goal-Question-Metric (GQM). IJCSNS International Journal of Computer Science and Network Security 8(5) (2008)
Gruhn, V., Laue, R.: Approaches for Business Process Model Complexity Metrics. In: Abramowicz, W., Mayr, H.C. (eds.) Technologies For Business Information Systems, pp. 13–24 (2007), doi:10.1007/1-4020-5634-6_2
Guceglioglu, A.S., Demirors, O.: Using Software Quality Characteristics to Measure Business Process Quality. In: van der Aalst, W.M.P., Benatallah, B., Casati, F., Curbera, F. (eds.) BPM 2005. LNCS, vol. 3649, pp. 374–379. Springer, Heidelberg (2005)
ISO/IEC: 19761:2003 Software engineering- COSMIC-FFP- A functional size measurement method. International Organization for Standardization, Switzerland (2003)
Kaya, M., Demirörs, O.: E-Cosmic: A Business Process Model Based Functional Size Estimation Approach. In: 37th EUROMICRO Conference Software Engineering and Advanced Applications, SEAA, pp. 404–410 (2011)
Laue, R., Mendling, J.: Structuredness and its significance for correctness of process models. Information Systems and E-Business Management 8(3), 287–307 (2010), doi:10.1007/s10257-009-0120-x
Lavazza, L., Del Bianco, V.: A case study in COSMIC functional size measurement: The rice cooker revisited. In: Abran, A., Braungarten, R., Dumke, R.R., Cuadrado-Gallego, J.J., Brunekreef, J. (eds.) IWSM 2009. LNCS, vol. 5891, pp. 101–121. Springer, Heidelberg (2009)
Marín, B., Giachetti, G., Pastor, Ó.: Measurement of Functional Size in Conceptual Models: A Survey of Measurement Procedures Based on COSMIC. In: Dumke, R.R., Braungarten, R., Büren, G., Abran, A., Cuadrado-Gallego, J.J. (eds.) IWSM 2008. LNCS, vol. 5338, pp. 170–183. Springer, Heidelberg (2008)
Mendling, J.: Validation of Metrics as Error Predictors. In: Metrics for Process Models. LNBIP, vol. 6, pp. 135–150. Springer, Heidelberg (2009)
Mendling, J.: Metrics for Business Process Models. In: Metrics for Process Models. LNBIP, vol. 6, pp. 103–133. Springer, Heidelberg (2009)
Monsalve, C., Abran, A., April, A.: Measuring Software Functional Size from Business Process Models. International Journal of Software Engineering and Knowledge Engineering 21(3), 311–338 (2011), doi:10.1142/S0218194011005359
OMG, OMG business process model and notation (BPMN), version 1.2 (Object Management Group) (2009)
Reynoso, L., Rolón, E., Genero, M., García, F., Ruiz, F., Piattini, M.: Formal Definition of Measures for BPMN Models. In: Abran, A., Braungarten, R., Dumke, R.R., Cuadrado-Gallego, J.J., Brunekreef, J. (eds.) IWSM 2009. LNCS, vol. 5891, pp. 285–306. Springer, Heidelberg (2009)
Vanderfeesten, I., Cardoso, J., Mendling, J., Reijers, H., van der Aalst, W.: Quality Metrics for Business Process Models. In: Fischer, L. (ed.) 2007 BPM & Workflow Handbook, Workflow Management Coalition, Lighthouse Point, Florida, USA, pp. 179–190 (2007)
Vanderfeesten, I., Reijers, H.A., Mendling, J., van der Aalst, W.M.P., Cardoso, J.: On a Quest for Good Process Models: The Cross-Connectivity Metric. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 480–494. Springer, Heidelberg (2008)
Mendling, J., Reijers, H., van der Aalst, W.: Seven Process Modeling Guidelines (7PMG). Information and Software Technology Journal 52, 127–136 (2010)
Conte, S., Dunsmore, H., Shen, V.: Software Engineering Metrics and Models. Benjamin-Cummings, Menlo Park (1986)
Frakes, W.B., Succi, G.: An industrial study of reuse, quality, and productivity. Journal of Systems and Software 57(2), 99–106 (2001), doi:10.1016/S0164-1212(00)00121-7, ISSN 0164-1212
Moseley, C.W.: A timescale estimating model for rule-based systems. Ph.D. diss., North Tex. State Univ. (1987)
Rainer, A., Hall, T.: Key success factors for implementing software process improvement: a maturity-based analysis. Journal of Systems and Software 62(2), 71–84 (2002), doi:10.1016/S0164-1212(01)00122-4, ISSN 0164-1212
Potok, T.E., Vouk, M., Rindos, A.: Productivity analysis of object-oriented software developed in a commercial environment. Software – Practice and Experience 29(10), 833–847 (1999)
McBride, T., Henderson-Sellers, B., Zowghi, D.: Software development as a design or a production project: An empirical study of project monitoring and control. Journal of Enterprise Information Management 20(1), 70–82 (2007)
Muketha, G.M., Ghani, A.A.A., Selamat, M.H., Atan, R.: A Survey of Business Process Complexity Metrics. Information Technology Journal 9(7), 1336–1344 (2010)
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Aysolmaz, B., İren, D., Demirörs, O. (2013). An Effort Prediction Model Based on BPM Measures for Process Automation. In: Nurcan, S., et al. Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2013 2013. Lecture Notes in Business Information Processing, vol 147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38484-4_12
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