An Empirical Study on Data Flow Bugs in Business Processes

Abstract

An increasing number of service-based business processes are being developed with the booming of BPaaS (Business Process as a Service) in cloud computing. The profits and performance of enterprises strongly depend on the soundness of their processes being bereft of control flow and data flow bugs. Although some work has focused on the detection of control flow bugs, few studies have comprehensively and empirically investigated data flow bugs in business processes. To this end, we report an empirical study on data flow bugs in business (BPEL) processes. Our analysis of 178 real-world BPEL processes reveals that data flow bugs are surprisingly common: 94 BPEL processes involve data flow bugs, among which redundant output is predominant. The distribution and common scenarios of data flow bugs provide a reference for BPEL process designers. We also investigate the correlation between process complexity metrics and data flow bugs. Based on the statistics of the process complexity metrics and data flow bugs in our empirical study, we present a method to select appropriate metrics as features of BPEL processes and utilize state-of-the-art supervised learning algorithms to predict data flow bugs in an unseen BPEL process. The prediction accuracies of the different classification algorithms exceed 90 percent on average when using our selected metrics.