An effort prediction model based on BPM measures for process automation

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.