With the rapid advancements in medicine, biology, and information technology, their deep integration has given rise to the emerging field of bioinformatics. In this process, high–throughput technologies such as genomics, transcriptomics, and proteomics have generated massive volumes of biological data. The biological significance of these data heavily relies on bioinformatics software for analysis and processing. Therefore, it is crucial for both scientific research and clinical applications to ensure the quality of bioinformatics software and avoiding errors or hidden defects. However, to date, no dedicated study has systematically analyzed the quality of bioinformatics software. we conduct a comprehensive empirical study that aggregates, synthesizes, and analyzes findings from 167 bioinformatics software projects. Following the Preferred Reporting Items for Systematic Review and Meta–Analysis (PRISMA) protocol, we extract and evaluate quality–related data to answer our research questions (RQs). Our analysis reveals several key findings. The quality of bioinformatics software requires significant improvement, with an average defect density approximately 11.8× higher than that of general-purpose software. Additionally, unlike traditional software domains, a considerable proportion of defects in bioinformatics software are related to annotations. These issues can lead developers to overlook potential security vulnerabilities or make incorrect fixes, thereby increasing the cost and complexity of subsequent code maintenance. Based on these findings, we further discuss the challenges faced by bioinformatics software and propose potential solutions. This paper lays a foundation for further research on software quality in the bioinformatics domain and offers actionable insights for researchers and practitioners alike.

ABSTRACT Business process monitoring involves real‐time supervision of a series of activities carried out by an organization to achieve specific objectives. Process event monitoring points (PEMP) are used to pinpoint specific locations within a process model where expected events are anticipated to occur. However, in general business process modeling languages, such as business process model and notation (BPMN), there is a lack of explicit modeling for PEMPs. This article proposes a method for modeling pairwise event monitoring points in business process models to track the execution status of specific activities or process segments via BPMN extension. Specifically, process monitoring points are designed by expanding the modeling element of sequence flow. Moreover, the business process model is decomposed using the refined process structure tree (RPST) to verify the soundness of the designed pairwise monitoring points. The proposed method allows flexible monitoring at the process segment level. And the process monitoring data could be used for a clear understanding of business progress, especially useful in heterogeneous business process model to support decision‐making. Through case study from real‐world business processes, the effectiveness and usefulness of the proposed process monitoring modeling method is validated.

Business process modeling is widely used in modern organizations for business description. Business Process Modeling Notation (BPMN), as a de-facto modeling standard, represents business process models in graphical notations. Nevertheless, BPMN lacks intuitive modeling tasks for Industrial Internet application scenarios (e.g., IoT tasks and multi-instance tasks with constraints). Although there are some works on extending BPMN elements to improve the model representation, most of them stay in the conceptual model only without tool support, or they are confined to specific domains. In this paper, we extend both BPMN elements and attributes for application in the Industrial Internet context, and two modeling tools are implemented in a client version and Web version to support business process modeling via low-code, enabling the BPMN extension model from conceptual to executable level. Two real-world case studies in Industrial Internet are conducted to show the usefulness of process models with BPMN extension. Furthermore, a comprehensive user experiment is conducted to evaluate the extended process models and tools, and the experimental results show that process models with extension have better quality compared with traditional process models and provided tools are effective for business process modeling in Industrial Internet applicationsNote to Practitioners—This article was motivated by the problem of business process modeling for Industrial Internet. Practically, existing methods extend BPMN elements and add text annotations to enhance the representation of process models. Although these methods facilitate process participants to understand the process models in detail, most of them focus on conceptual modeling and increase the complexity of process models. Moreover, the process models and multi-instance business constraints of business processes involving IoT elements are difficult to be represented by using native BPMN. This article proposes a comprehensive business process modeling language, named BPMN$++$, which extend both new modeling elements for Industrial Internet business requirements and attributes for BPMN multi-instance tasks with the corresponding graphical notations. Then, BPMN$++$model could be executed on the process engine, enabling the model from conceptual to executable level. Finally, comprehensive experiments are conducted for BPMN$++$.

With the wide adoption of Web APIs released on Internet, users tend to reuse them for business requirements or software development. Mashup is a useful technology for composing Web APIs into a new and value-added application. With the increasing number of Web APIs and Mashups, the API-Mashup ecosystem has emerged based on the invocation relationship between Mashups and Web APIs. In this paper, we take ProgrammableWeb, a typical API-Mashup ecosystem, as an example to investigate its dynamic evolutionary analysis. Although there have been some works on the API-Mashup ecosystem, they mainly focus on static analysis, i.e., the static characteristics of the API- Mashup ecosystem on a fixed time point. This paper conducts a comprehensive study on the dynamic evolutionary analysis of the API-Mashup ecosystem with a long time range from 2005 to 2021. First, we conduct a dynamic statistical analysis based on the API-Mashup ecosystem dataset. Next, we construct two cooperation networks, one between Web APIs, and the other between their categories. And the general characteristics of the two cooperation networks are presented. Finally, we investigate the derived cooperation networks from four perspectives: dynamic characteristics, degree distribution, betweenness centrality, and assortative mixing. Meanwhile, the corresponding insights are uncovered. Our work provides a foundation for visualization and understanding of the API-Mashup ecosystem from the timeline.

Business process monitoring aims at identifying how well running processes are performing with respect to performance measures and objectives. The existing business process monitoring techniques focus on collecting and analyzing information on the way business processes themselves are executed. They neglect the evaluation of task-roles which play a key role in the performance of the whole business process execution. Different from the traditional perspective, this paper focuses on monitoring the behavior of task-roles and evaluating their performance with respect to timeliness. Specifically, this paper proposes to promote the performance of task-roles by time reminder via business process monitoring, which is implemented by semantic extension of BPMN elements. Further, we extend the information of process execution event log data, with which the performance of task-roles can be evaluated by analyzing the extended event log data. An empirical study of the proposed approach with real-world business processes reveals the effectiveness with respect to performance evaluation of task-roles.