However, organizations often maintain hundreds or even thousands of them, which makes a manual analysis unfeasible for larger organizations. Textual process descriptions, such as work instructions, provide rich and important insights about this matter. While the benefits of RPA on cost savings and other relevant performance indicators have been demonstrated in di↵erent contexts, one of the key challenges for RPA endeavors is to effectively identify processes and tasks that are suitable for automation. RPA solutions emerge in the form of software that automatically executes repetitive and routine tasks. Among others, this has lead to an increased interest in Robotic Process Automation (RPA). The continuous digitization requires organizations to improve the automation of their business processes. Therefore, our approach provides automated support for an otherwise tedious and complex manual endeavor. A quantitative evaluation shows that our approach is able to generate constraints that closely resemble those established by humans. To achieve this, we developed tailored Natural Language Processing techniques that identify activities and their interrelations from textual constraint descriptions. In this paper we close this gap by presenting the first automated approach for the extraction of declarative process models from natural language. The extraction of declarative process models, which allow to effectively capture complex process behavior in a compact fashion, has not been addressed. However, these techniques, so far, only focus on the extraction of traditional, imperative process models. Since the manual elicitation and creation of process models is a time-intensive endeavor, a variety of techniques have been developed that automatically derive process models from textual process descriptions. Process models are an important means to capture information on organizational operations and often represent the starting point for process analysis and improvement. The experiments resulted in a suitable BPMN diagram with higher accuracy than obtained by other methods. The proposed method was applied to ten textual requirements of an enterprise application, which involved simple, compound, complex, and compound-complex sentences. The BPMN diagram is generated using a set of informal mapping rules that were created in this study. The output of the first stage is fact types as the basis for generating the BPMN diagram in the second phase. The proposed method has two stages: 1) analyzing the textual requirements using natural language processing and 2) generating the BPMN diagram. This study proposes conversion from textual requirements to a BPMN diagram for improving the weaknesses of existing methods. The methods currently used for converting NL input to BPMN diagrams are not able to generate complete BPMN diagrams, nor can they handle complex and compound-complex sentences in the NL input. In this study, the BPMN diagram is used as an intermediate representation before measuring the functional software size from Natural Language (NL) input. The results show that the proposed approach was able to generate models that are more than 81% similar to those created manually by a human, outperforming the state of the art in this topic.Īn interesting challenge in software requirements engineering is converting textual requirements to Business Process Model and Notation (BPMN) diagrams. The approach has been implemented and evaluated using a similarity metric based on the Graph Edit Distance. We achieve our goal in the second phase via a set of semantic, syntactic, and morphological manipulations. This map represents a background for our processing in the second phase where we try to generate the "translation", which is the BPMN diagram in our case. One of the main outputs for this phase is a Concept Map which summarizes the concepts of the related domain and the relationships between these concepts. Natural Language Analysis phase aims to analyze the text and extract the required knowledge. Our approach consists of two main phases: The natural Language Analysis phase and BPMN diagram generation. We chose to follow a semantic transfer-based MT approach. In this paper, we propose a Machine Translation (MT) like approach to deal with the problem of generating a business process model based on a textual description. These challenges raised the question of how researchers can save this cost by building tools that could support modeling experts in their work to reduce the manual workload. It represents the most critical step in the BPM life cycle and is considered as a time consuming and costly task. Modeling is one of the core tasks in Business Process Management (BPM).
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