International audience

Process Management Systems (PMS) offer effective means to coordinate tasks for various teams involved in complex projects. However, in practice, participants execute their tasks using applications within their workspace and manually report their activities to a separate PMS. This approach is not only time-consuming for end-users but also poses reliability challenges for the PMS in verifying the accuracy of reported task completion. This paper proposes an AI-augmented framework to establish seamless integration between PMSs and end-user workspace, which can automatically manage process execution by intelligently monitoring the tasks of process participants. We examined the utilization of our framework via a prototype pMage in a software implementation process. The results showcase the capability in managing process progress in various scenarios. The service effortlessly integrates the working environment with the corresponding process while maintaining its low-code applicability and independence from specific PMS or end-user workspace. This framework is anticipated to encourage end-users to embrace PMS as an integral part of their daily work, thereby unlocking the benefits of a process-aware workspace.

International audience

International audience

International audience

During the execution of a process, managing the collaboration inside a task performed by various actors is not straightforward due to possible changes of the process's context and the collaboration strategy. Process management solutions which describe the collaboration at modeling time offer a rigid control for conducting such collaborative tasks and thus cannot adapt to changes. To enable a flexible execution of collaborative tasks, we propose using the late-binding mechanism to allow process actors, at execution time, choosing or adapting strategies to perform their collaboration. To do so, first we model collaboration strategies as process patterns providing different ways to implement a collaborative tasks at execution time. These collaboration patterns describe how to establish necessary relations for coordinating different instances of the task, for sharing and exchanging working artifacts among actors performing those instances. Then we define actions to execute collaborative tasks. These actions take collaboration patterns as parameters. Thus, by letting process actor selecting a suitable collaboration pattern, they allow binding dynamically a collaborative task to its implementation flexibly.

Collaborative work is a complex activity that involves several actors thus managing it is complicated. This article addresses some matters of controlling collaborative work in the point of view of process management. The underlying objective is to discuss the issues from modeling to enacting collaborative processes in existing process management systems. To overcome the discussed problems, we are developing a process management framework that integrates directly the concept of collaborative task into both modeling language and enacting mechanism. Moreover, we propose a flexible approach that enables users to choose dynamically a suitable way to enact their collaborative tasks.

Abstract In System and Software Engineering development, unforeseen changes occurring during process enactment are almost inevitable but often poorly managed due to a lack of efficient mechanisms for spontaneously handling these run‐time changes. We proposed a change‐aware process management system that allows process actors reporting emergent changes, analyzing possible impacts, and notifying people affected by the changes. To this end, we integrated a Change Management Component with a Process Management System. The Process Management System monitors process enactment and uses the run‐time process information to construct a Process dependency graph (PDG) representing the dependencies among the elements of running processes. The Change Management Component captures change requests sent asynchronously, then reasons the PDG to determine impacted elements. Our PDG reflects the information of process instances and therefore can uncover the intra‐process or inter‐processes dependencies that are invisible on process models. We implemented a prototype named CAPE based on the platform jBPM and the graph database Neo4j. Copyright © 2016 John Wiley & Sons, Ltd.

Managing changes for knowledge-intensive processes like System and Software Engineering is a critical issue but far from being mastered due to the lack of supporting methods and practical tools. To manage changes systematically, a process environment is needed to control processes and to handle changes at run-time. However, such an effective environment satisfying these requirements is still missing. The reason is two-folds: first, operational process environments for system and software engineering is scarce; second, there is a lack of efficient change management mechanism integrated in such process environments. In order to address these concerns, we aimed at developing a change-aware process environment for system and software engineering. To this aim, we proposed a change management mechanism based on (1) the Process Dependency Graph (PDG) representing the dependencies among running process instances managed by a process environment ; (2) a Change Observer process to catch change events and update the PDG with run-time information; (3) a Change Analyzer component to extract the impacts of change by reasoning the PDG. In terms of implementation, to gain the benefits from the Business Process Community, where many mature Business Process Management Systems have been developed, we chose jBPM to enact and monitor processes. The key strengths of this study are: first, the PDG makes hidden dependencies among process instances emerge at run-time; second, the process observer inside the BPMS allows to handle the change events in a timely manner. Finally, the Neo4j graph database, used to store the PDG, enables efficient traversal and queries.

Modern software and system collaborative process involves various teams in different development phases thus need efficient solutions for tools integration. In Model-Driven Development, transformation technique is used to allow exchanging models created by different tools. However, in a process, transformation are often defined manually for a tool-incompatible point and rarely reusable. To facilitate the automatic generation of transformation rules for tool integration, we propose to use process ontology together with process editor when modelling process. The idea is using ontology to stock process assets from various sources so that the relations between similar elements in different technical spaces can be established automatically. The process editor enriches the ontology by process elements captured from modelling activities. Then the integrated ontology helps the editor detect tool integration points and complete the process model as well as generate the mappings between concerned process elements.

International audience

In complex domains with features of collaboration and integration, changes taking place in one process may have positive or negative impacts on other collaborative partners and processes. Therefore, supporting changes propagation as well as analysis the impact of changes are desirable functionalities of Business Process Management Systems (BPMSs) and have been investigated in many researches. This paper presents a method to analyze the impacts of process changes at run-time in order to help process practitioners deciding to adopt or not a change. We propose an approach based on Process Dependency Graph (PDG) to represent and monitor the dependencies among running process instances managed by a BPMS. When a change happens, we analyze the PDG graph to deduce the affected process elements and then assess the impact of change by using quantitative metrics extended from the workflow Quality of Service (QoS). Our approach thus provides a generic framework that can be adapted to a specific process domain and a BPMS. The work presented here was validated on some processes in Health-care and Software Development domains with the use of jBPM as a BPMS and Neo4j as a graph database to store and traverse the PDG.

We report in this paper a method for proving that a graph transformation is property-preserving. Our approach uses a relational representation for graph grammar and a logical representation for graph properties with first-order logic formulas. The presented work consists in identifying the general conditions for a graph grammar to preserve graph properties, in particular structural properties. We aim to implement all the relevant notions of graph grammar in the Isabelle/HOL proof assistant in order to allow a (semi) automatic verification of graph transformation with a reasonable complexity. Given an input graph and a set of graph transformation rules, we can use mathematical induction strategies to verify statically if the transformation preserves a particular property of the initial graph. The main highlight of our approach is that such a verification is done without calculating the resulting graph and thus without using a transformation engine.

In software engineering, as in any collective endeavor, understanding and supporting collaboration is a major concern. Unfortunately, the main concepts of popular process formalisms are not always adequate to describe collaboration. We extend the Software & System Process Engineering Meta-Model (SPEM) by introducing concepts needed to represent precise and dynamic collaboration setups that practitioners create to address ever-changing challenges. Our goal is to give practitioners the ability to express evolving understanding about collaboration in a formalism suited for easy representation and tool-provided assistance. Our work is based on a collaborative process metamodel we have developed. In this paper, we first present a meta-process for process modeling and enactment, which we apply to our collaborative process metamodel. Then we describe the implementation of a suitable process model editor, and a project plan generator from process models.

Process pattern is an emerging approach for process reuse. Representing process models based on process patterns to explicit process solutions and factor recurrent process constituents is useful for process understanding as well as process modeling. This subject, however, is still a challenge for the software process technology community. In this paper, we present a UML-based process meta-model that allows explicit representation of process patterns in process models. The novel of our proposition is to enable the application of different process knowledge kinds not only for building but also for improving process models.