Detecting anomalous artifact operations in workflows at design time is essential for preventing errors and unexpected behavior during execution. Workflow designers aim to maximize concurrency to enhance efficiency and quality. However, concurrent operations on shared artifacts, such as simultaneous read and write, can lead to anomalies like race conditions that compromise artifact integrity. Existing methods are often limited in their ability to detect such anomalies due to inaccurate anomaly definitions and inefficiencies caused by high space complexity. In this work, we introduce an approach to effectively detect concurrent anomalies leveraging the SP-tree, a tree-like structure that captures both control flow and artifact operations in workflows. We redefine concurrent anomalous behaviors based on the SP-tree framework and develop efficient algorithms to identify these anomalies. Our method operates in two stages: (1) transforming the workflow into an SP-tree and (2) analyzing the SP-tree using our anomaly detection algorithms. Compared with existing techniques, our approach detects all artifact anomalies, reduces time complexity, and simplifies the anomaly detection process. By adopting our approach, workflow designers can more accurately identify concurrent artifact anomalies, reducing the risk of runtime errors and improving the overall reliability of the workflow.

Microservice-Based Workflows (MBWs) are used popularly to govern the composition and coordination of individual microservices to realize business processes. With MBWs, designers often aim to maximize concurrency to increase the chances of successful workflow collaboration and enhance business process efficiency. However, operations involving manipulating and accessing artifacts (data objects) within these workflows may introduce anomalies leading to unexpected artifact states. In workflow design phase, seeking bug-free artifact states is vital to help prevent crashes, errors, and unexpected outcomes during execution. Concurrent artifact anomalies are referred to abnormal parallel operations on the same artifact. Few studies have explored the detection of concurrent anomalies in MBWs, and they are inefficient and ineffective as they struggle with high time complexity and are insensitive to the presence of nested AND gateways. This paper focuses on improving microservices workflows reliability by detecting concurrent anomalies in artifacts during the design. We present a series of methods to detect the anomalies based on SP-tree, a tree structure to record workflow paths and artifact information. Our methods outperform existing ones by detecting more anomalies with lower time and space complexity.

During the design phase of a workflow process, detecting anomalous operations on artifacts is important for preventing errors and unexpected behaviors of the process dynamically. For temporal structured workflows (TSWs), which involve specifying the min and max execution time intervals for each activity, there are few studies on analyzing artifact anomalies compared to non-temporal workflows. Additionally, the existing approaches designed for TSWs are inaccurate in detecting these anomalies either. To improve the analysis of TSWs, this paper presents an improved methodology based on an extended SP-tree structure, called TSP-tree. Our approach involves two steps: first, transforming a TSW into a TSP-tree, and then applying several algorithms to TSP-tree to detect artifact anomalies. Compared to previous methods, our approach provides lower time complexity for detection, simplifies the analysis, and detects more artifact anomalies