Though ensuring logical consistency between flows in use case specifications (UCSs) and high‐level business processes is a prerequisite and foundation for generating high‐quality, full‐coverage test cases, this task still primarily relies on manual effort. To address this limitation, a large language model (LLM)‐assisted test‐driven approach is proposed, which introduces a formal structure to guide LLMs in generating UCSs based on natural language requirements. Specifically, to validate the consistency of UCS flows with high‐level business process logic, UML activity and state machine diagrams are used as specific modeling methods for such processes. Furthermore, three novel rules are proposed to validate the consistency: business objects are incorporated to not only horizontally connect UCSs with these models but also vertically bridge different abstraction levels of requirements. To normalize this methodology, a comprehensive framework is established—this framework enables bidirectional traceability between requirements and testing while simultaneously building a feedback loop that integrates requirements, testing, and process models. Experimental results show that the approach not only improves the requirements specification quality but also facilitates semi‐automatic generation of test cases for acceptance testing, thereby enhancing the efficiency of the overall development process.

Analyzing the temporal behaviors and revealing the hidden rules of objects that produce time series data to detect the events that users are interested in have recently received a large amount of attention. Generally, in various application scenarios and most research works, the equal interval sampling of a time series is a requirement. However, this requirement is difficult to guarantee because of the presence of sampling errors in most situations. In this paper, a multigranularity event detection method for an unequal interval time series, called SSED (self-adaptive segmenting based event detection), is proposed. First, in view of the trend features of a time series, a self-adaptive segmenting algorithm is proposed to divide a time series into unfixed-length segmentations based on the trends. Then, by clustering the segmentations and mapping the clusters to different identical symbols, a symbol sequence is built. Finally, based on unfixed-length segmentations, the multigranularity events in the discrete symbol sequence are detected using a tree structure. The SSED is compared to two previous methods with ten public datasets. In addition, the SSED is applied to the public transport systems in Xiamen, China, using bus-speed time-series data. The experimental results show that the SSED can achieve higher efficiency and accuracy than existing algorithms.

In collaborative tasks, the composition of distributed resource services can improve the resource utilization. From the perspective of a business process, resource services should be composed as service flow so as to serve better a business process. However, most of the existing methods neglect an important factor, the characteristic of interorganization, which should be taken into consideration. An interorganizational resource service sequence is not necessarily available to all participating organizations in a collaborative task, as each organization seeks and selects resource services independently. Therefore, resource services in sequential order are called the resource service chain (RSC). This problem is called resource service chain composition with inter-organizational collaboration (RSCCOrg). The collaborative manufacturing is taken as an instance because it is a typical collaborative task. The proposed approach here is composed of several algorithms, called the algorithms for RSCCOrg (ARSCCOrg) and can better cope with interorganizational collaboration. To begin, a model based on a weighted directed graph is presented for the resource service temporal dependences. This is convenient for describing the temporal dependences among resource services and obtaining frequent RSCs. By calculating the frequencies between every two resource services from business data, the degrees of temporal dependence between them are resolved. A set of frequent RSCs can be obtained by choosing the higher weights. Next, an algorithm is presented to extend the frequent RSCs to increase the reusability and to improve the efficiency of selection. Then, a similarity formula is presented to compare the similarities between the frequent RSCs and the extended RSCs. In particular, the characteristic of interorganization is considered in the similarity comparison. The ARSCCOrg has been tested with a practical dataset, and the results show that it is very promising.

Resource positioning in cloud computing system under the support of the Internet of things (IoT) is very important, especially for distributed collaborative task. Position-based services is used to collect, process, store and distribute position information. Taking a typical collaborative task - cloud manufacturing as the background, in view of resource positioning service, an algorithm supported by Wireless Sensor Networks (WSN) is proposed, called WOCA (Weighted Optimized Combination Algorithm). Firstly, n signals received from base station are divided into C(n,3) groups, and the positioning result obtained by trilateral positioning algorithm respectively. Next, considering the plurality of weighting factors, the proper ratio coefficient is assigned to each of them by the degree of influence on the accuracy, and then the weights of each group were calculated accordingly. Finally, obtaining weighted positioning result by computing each group with the corresponding weights. The experimental results show the validity of the proposed algorithm.

One of the important goals collaborative tasks achieve is to improve the entire efficiency of business processes. The influence should be discovered between resource services according to the order the activities use in business processes. However, it is especially difficult in collaborative task as each participant organizations use resource services independently. In a workflow, resource services are used in some sequence. An approach for selecting key feature sequence based on mutual information (KFSS-MI) is proposed. Firstly, by analyzing features of resource services, feature sequence and key feature sequence are defined. Then, based on mutual information, an algorithm is proposed to find key feature sequences from business data. The determined thresholds is required, that are the minimal support and the minimal influence degree. Finally, a simulated experiment is used to test our approach. The experimental results confirm the validity and efficiency of the approach.

In collaborative task, composition of resource services can be provided to enterprise users to enable value-added services. Taking typical cloud manufacturing for instance, the composition of resource services can improve the efficiency of resource selection and usage as massive manufacturing resource are widely distributed. To achieve the goal, a reasonable approach for partitioning aggregation of resources services in a business process is very important for the composition. Firstly, depending on workflow model, a correlative matrix is built according to the dependencies between different resource services. Then, an algorithm based on the Bond Energy Algorithm (BEA) is proposed to transform and partition the matrix. The sets of resource services with high interdependencies are aggregated, as a basis for different compositions of resource services. Finally, taking a collaborative design and manufacturing process as a case to shows that the proposed method is available.

Though cloud computing technology is usually used to analyze big data, for business data produced by collaborative task system, the massive business data sets should be analyzed from a business process perspective, more than a technology perspective. To achieve the goal, an analysis approach based on iterative computation is proposed. Firstly, taking data sequence analysis in collaborative workflows for instance, massive business data is partitioned horizontally and vertically. In the horizontal level, the complete workflow is considered as the basic unit of data analysis. In the vertical level, massive business data is partitioned to segments, the size of which depends on the load capacity. Then, sequential relationship matrix is used to represent the relationship between data sets, and the data sequences are calculated iteratively segment by segment and combined. The results of the analysis are represented as a more accurate data sequence model. Finally, the proposed approach has been tested with a simulation experiments and a case analysis respectively.

In distributed manufacturing systems, manufacturing resource composition is one of the most important problems. This is because efficiency of resource selection and resource utilization can all be improved if it is tackled well. However, most of the existing methods neglect temporal relationship between resources. This leads to an inefficient use of resources, because all resources have to be kept available before a business process is started. A temporal composition of resources is more suitable, as it expresses the scheduling and the flow of servicing to a business process. Therefore, resource services invoked in sequential order are called the resource-service chain (RSC), in view that distributed resources are encapsulated into cloud services in a cloud manufacturing (CMfg) environment. We propose an approach, called RSC composition algorithm (RSCCA) that can better cope with the temporal relationship between the resource services in a business process. Specifically, a two-stage composition method based on the degrees of dependency between resource services in workflow is proposed. To begin, in the build-time stage algorithm, RSCCA resolves initial compositions based on task relatedness and temporal dependencies between resource services, and then calculates the usage frequencies of ICs by mining workflow log at workflow runtime stage. Based on this, RSCCA can compose individual resource services as more than sets, especially as chains, allowing flow directions and dynamics to be considered. RSCCA has been tested with different data sets and the results show that it can be very promising.