A Comprehensive Investigation of BPMN Models Generation from Textual Requirements—Techniques, Tools and Trends

Maqbool, Bilal; Azam, Farooque; Anwar, Muhammad Waseem; Butt, Wasi Haider; Zeb, Jahan; Zafar, Iqra; Nazir, Aiman Khan; Umair, Zuneera

doi:10.1007/978-981-13-1056-0_54

Cited by 20 publications

(7 citation statements)

References 28 publications

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“…A Systematic Literature Review (SLR) that proposes a comprehensive investigation of the techniques and tools adopted in the research for the generation of BPMN models from text is proposed in Maqbool et al [2018]. The main claim the authors made from their analysis is that existing approaches are still immature to be adopted in real settings such as industry.…”

Section: Related Workmentioning

confidence: 99%

“…Process Discovery [van der Aalst, 2011] refers to the set of algorithmic methodologies that aim to automatically extract and a process model as described in data. While the typical source of data is that of event-logs, and Process Discovery from event-log data is a well established and mature area that has already moved from research to concrete adoption in a mature manner, Process Discovery that uses textual documents as a source of data is a research area which still lacks the ability to scale to generic real world texts [Maqbool et al, 2018, van der Aa et al, 2018a 1 .…”

Section: Introductionmentioning

confidence: 99%

“…The ambiguous nature of natural language, the multiple possible writing styles, and the great variability of possible domains of application make this task extremely challenging. Indeed recent papers on this topic [Riefer et al, 2016, Maqbool et al, 2018, van der Aa et al, 2018a highlight that after almost ten years of research, process extraction from text is a task far from being resolved and further research in this direction is needed with the purpose of improving the quality of the process model generation.…”

Section: Introductionmentioning

confidence: 99%

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Process Extraction from Text: state of the art and challenges for the future

Bellan,

Dragoni,

Ghidini

2021

Preprint

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Automatic Process Discovery aims at developing algorithmic methodologies for the extraction and elicitation of process models as described in data. While Process Discovery from event-log data is a well established area, that has already moved from research to concrete adoption in a mature manner, Process Discovery from text is still a research area at an early stage of development, which rarely scales to real world documents. In this paper we analyze, in a comparative manner, reference state-of-the-art literature, especially for what concerns the techniques used, the process elements extracted and the evaluations performed. As a result of the analysis we discuss important limitations that hamper the exploitation of recent Natural Language Processing techniques in this field and we discuss fundamental limitations and challenges for the future concerning the datasets, the techniques, the experimental evaluations, and the pipelines currently adopted and to be developed in the future.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Process Extraction from Text: state of the art and challenges for the future

Bellan,

Dragoni,

Ghidini

2021

Preprint

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“…On the opposite side of the spectrum, there are approaches that autonomously convert a textual description of a process model into a formal representation [13]. Such representation can be a final process model (e.g., as BPMN) [7] and, in this case, it might be possible to automatically extract in-formation.…”

Section: Related Workmentioning

confidence: 99%

Formal Reasoning on Natural Language Descriptions of Processes

Sànchez-Ferreres

Burattin

Carmona

et al. 2019

Lecture Notes in Computer Science

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The existence of unstructured information that describes processes represents a challenge in organizations, mainly because this data cannot be directly referred into process-aware ecosystems due to ambiguities. Still, this information is important, since it encompasses aspects of a process that are left out when formalizing it on a particular modelling notation. This paper picks up this challenge and faces the problem of ambiguities by acknowledging its existence and mitigating it. Specifically, we propose a framework to partially automate the elicitation of a formal representation of a textual process description, via text annotation techniques on top of natural language processing. The result is the ATDP language, whose syntax and semantics are described in this paper. ATDP allows to explicitly cope with several interpretations of the same textual description of a process model. Moreover, we link the ATDP language to a formal reasoning engine and show several use cases. A prototype tool enabling the complete methodology has been implemented, and several examples using the tool are provided.

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“…Natural language processing is one of the major areas within the domain of artificial intelligence. Main NLP techniques include Sentence splitting, chunking, tokenizing, parsing, POS tagging [28].…”

Section: B Literature Reviewmentioning

confidence: 99%

A Novel Framework to Automatically Generate IFML Models From Plain Text Requirements

et al. 2019

Self Cite

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User Interfaces (UI's) are highly important in this era of web and mobile applications. Therefore, an efficient and accurate development of UI's is desirable in early Software Development Life Cycle (SDLC) phases. To achieve this, Object Management Group (OMG) introduced Interaction Flow Modeling Language (IFML) standard in 2013. IFML provides the modeling of manifold UI's for different applications like mobile, web and desktop. Although IFML is based on Model Driven Engineering (MDE) principle, the development of user interface models from initial requirements is still complex and time consuming task. Particularly, it requires domain expertise to understand several IFML concepts like domain model, view container etc. for the proper modeling of user interfaces. Consequently, there is a strong need of an approach to automate the development of IFML models from initial plain text requirements. This article presents a novel framework to automatically generate IFML models from textual requirements by utilizing the features of Natural Language Processing (NLP). Particularly, a set of NLP rules are developed to extract important IFML elements like View Components, Events etc. from textual requirements. Furthermore, a comprehensive algorithm is developed for the systematic execution of NLP rules in order to generate both IFML Domain and Core models. As a part of research, a sophisticated T ext to IFML (T2IF) tool is developed. The feasibility of proposed framework is demonstrated through movie manager and online bookstore case studies. The evaluation results prove that the proposed framework is capable of generating IFML models from textual requirements with high accuracy.

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A Comprehensive Investigation of BPMN Models Generation from Textual Requirements—Techniques, Tools and Trends

Cited by 20 publications

References 28 publications

Process Extraction from Text: state of the art and challenges for the future

Process Extraction from Text: state of the art and challenges for the future

Formal Reasoning on Natural Language Descriptions of Processes

A Novel Framework to Automatically Generate IFML Models From Plain Text Requirements

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