Extracting SBVR business vocabularies and business rules from UML use case diagrams

Skersys, Tomas; Danėnas, Paulius; Butleris, Rimantas

doi:10.1016/j.jss.2018.03.061

Cited by 26 publications

(24 citation statements)

References 15 publications

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“…A preexisting UML class model is intended to be consistent with the vocabulary used in the textual constraints. T. Skersys [24] propose an hybrid approach to extract general concepts, verb concepts, and SBVR statements from formal use case, enhanced later by P.Danenas [25] using an NLP-based approach instead of purely template-based rules. P. K. Chittimalli [26] propose an approach to mine SBVR based business vocabularies and rules from domainspecific business documents.…”

Section: B Natural Languaue To Sbvrmentioning

confidence: 99%

The Semantic of Business Vocabulary and Business Rules: An Automatic Generation From Textual Statements

et al. 2021

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In the early phases of the software development process, specifications are mostly written in a natural language rather than formal models, which is not supported by the Model Driven Architecture (MDA). For this reason, the Semantic of Business Vocabulary and Rules (SBVR) is proposed by the Object Management Group to represent the textual specifications in a language comprehensible by both of humans and machines, to facilitate its integration in the MDA lifecycle. However, businesspeople are usually not familiar with SBVR standard. In this paper we present an approach to automatically transform textual business rules to an SBVR model, to facilitate its integration in nowadays information technology infrastructures. Our approach is distinguished from existing works in that it uses an in-depth Natural Language Processing to extract a more comprehensible SBVR model that includes the semantic formulation of each business rule statement, coupled with a Terminological Dictionary of extracted concepts, to which we have added further specifications such as definitions and synonyms. The evaluation of our approach shows that for three sets of business rules statements taken from different domains, we could generate the correct meaning with an average of F1-score exceeding 87%.

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Section: B Natural Languaue To Sbvrmentioning

confidence: 99%

The Semantic of Business Vocabulary and Business Rules: An Automatic Generation From Textual Statements

et al. 2021

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“…A. Awad [16] proposed an automated approach for querying a business process model repository, in which the most common sense was selected from WordNet [11] dictionary. Other approaches tried to extract synonyms using dictionary (like WordNet) without explanation neither about what was the algorithm used, nor if the context was taken into consideration or not, such in [17,18] in which SBVR business vocabularies and rules are extracted from UML use case diagrams; or [19] which presented an automatic approach to generate BPMN models from natural language text; Based entirely on dictionary results. (method not cited) T. Skersys [18] Use Case Diagram SBVR Business Vocabularies and Rules…”

Section: Related Workmentioning

confidence: 99%

Automated Identification of Semantic Similarity between Concepts of Textual Business Rules

Haj¹,

Balouki²,

Gadi³

2021

IJIES

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Business Rules (BR) are usually written by different stakeholders, which makes them vulnerable to contain different designations for a same concept. Such problem can be the source of a not well orchestrated behaviors. Whereas identification of synonyms is manual or totally neglected in most approaches dealing with natural language Business Rules. In this paper, we present an automated approach to identify semantic similarity between terms in textual BR using Natural Language Processing and knowledge-based algorithm refined using heuristics. Our method is unique in that it also identifies abbreviations/expansions (as a special case of synonym) which is not possible using a dictionary. Then, results are saved in a standard format (SBVR) for reusability purposes. Our approach was applied on more than 160 BR statements divided on three cases with an accuracy between 69% and 87% which suggests it to be an indispensable enhancement for other methods dealing with textual BR.

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“…Salah satunya diteliti oleh Elallaoui yaitu mentransformasi user stories menjadi bentuk use case menggunakan teknik NLP (Natural Language Processing) dengan keakuratan presisi diantara 87% dan 98% [8]. Penelitian Skersys, T. Mentransformasi bisnis vocabulary dan bisnis rule dari usecase model reprensentasi direpresentasikan kedalam bentuk use case diagram [9]. Penelitian Grangel, R. Mentransformasi Decisional Model ke dalam bentuk Use Case UML dengan hasil metamodel untuk mapping pertama kedalam UML Use Case [10].…”

Section: Analisisunclassified

Pengembangan Evsher (Event Publisher) Berbasis Android

Bawono

Safi’ie

Purbayu

et al. 2019

Simet

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Acara atau kegiatan akan selalu ada disekitar kita. Baik untuk kepentingan sosial ataupun yang kaitannya dengan bisnis. Keberhasilan sebuah acara dilihat dari banyaknya dan antusiasme dari pengunjung. Media penyebar informasi sangatlah penting untuk menyampaikan kepada masyarakat banyak akan adanya acara. Media yang banyak digunakan pada saat ini adalah smartphone. Aplikasi android pada smartphone yang digunakan untuk menyampaikan acara atau kegiatan adalah merupakan salah satu pemanfaatan teknolgi. Tujuan yang ingin dicapai adalah menerapkan teknologi android pada permasalahan publikasi kegiatan dengan menggunakan pengujian kompabilitas sistem sehingga sistem diharapkan dapat dijalankan di semua merek. Pengujian yang dilakukan agar sistem dipastikan berjalan dengan baik adalah dengan menguji sistem pada beberapa handphone dengan ukuran layar yang berbeda-beda. Aplikasi acara atau kegiatan telah berhasil dibuat dan berjalan dengan baik pada beberapa merek handphone yaitu samsung galaxy j3, samsung galaxy j5 prime, Xiaomi redmi 3.

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Extracting SBVR business vocabularies and business rules from UML use case diagrams

Cited by 26 publications

References 15 publications

The Semantic of Business Vocabulary and Business Rules: An Automatic Generation From Textual Statements

The Semantic of Business Vocabulary and Business Rules: An Automatic Generation From Textual Statements

Automated Identification of Semantic Similarity between Concepts of Textual Business Rules

Pengembangan Evsher (Event Publisher) Berbasis Android

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