Generating UML Diagrams from Natural Language Specifications

More, Priyanka; Phalnikar, Rashmi

doi:10.5120/ijais12-450222

Cited by 65 publications

(22 citation statements)

References 11 publications

(16 reference statements)

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“…However, the details like aggregation, association, generalisation are not extracted to generate a complete class diagram. An architecture of tool Requirement analysis to Provide Instant Diagrams (RAPID) (More and Phalnikar, 2012) follows the design structure of the tool UMGAR, but emphasis on the completeness, accuracy and flexibility of the class diagram. The authors present a wide range of rules viz.…”

Section: Natural Language To Object Oriented Modellingmentioning

confidence: 99%

“…As per our literature survey, there exists numerous tools viz. UMLG (Bajwa et al, 2009), RAPID (More and Phalnikar, 2012), SBVR2UML (Afreen et al, 2011) and UMGAR (Deeptimahanti and Sanyal, 2011) that take NLRs as input and output the UML counterpart. Tools such as UMLG, RAPID and SBVR2UML mainly focus on generating class diagrams from the informal requirements specifications.…”

Section: Introductionmentioning

confidence: 99%

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Resolving ambiguity in natural language specification to generate UML diagrams for requirements specification

Shah¹,

Jinwala

2015

IJSETA

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Requirements engineering activity often starts with the vaguely defined requirements. Hence, it is essential that when the requirements are analysed, the ambiguities are resolved to the extent possible. One of the attractive alternatives in resolving ambiguities is to convert the informal natural language requirements (NLRs) into their formal or semi-formal counterpart that ensures precision and orthogonality. Towards meeting this goal, the Unified Modelling Language (UML) notations can be exploited to an advantage. As per our literature survey, there exist numerous tools that take NLR as input and output the UML counterpart. However, most of the tools do not support normalising and resolving ambiguities. In this paper, we propose an architecture viz. ARUgen that is aimed to resolve ambiguities, find object oriented terms and relationship to generate accurate UML diagrams with Java code. Our tool ARUgen mainly deals with ambiguities viz. pronoun anaphora, verb anaphora and coordination.

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Section: Natural Language To Object Oriented Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resolving ambiguity in natural language specification to generate UML diagrams for requirements specification

Shah¹,

Jinwala

2015

IJSETA

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“…Examples include the (semi)-automatic creation of process models [31,37,35,34,77], conceptual dependency diagrams [33], entity-relationship models [36,66], and UML diagrams [3,18,19,64]. inconsistencies with requirement documents [11].…”

Section: State Of the Artmentioning

confidence: 99%

Detection of naming convention violations in process models for different languages

Leopold

Eid-Sabbagh

Mendling

et al. 2013

Decision Support Systems

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Companies increasingly use business process modeling for documenting and redesigning their operations.However, due to the size of such modeling initiatives, they often struggle with the quality assurance of their model collections. While many model properties can already be checked automatically, there is a notable gap of techniques for checking linguistic aspects such as naming conventions of process model elements. In this paper, we address this problem by introducing an automatic technique for detecting violations of naming conventions. This technique is based on text corpora and independent of linguistic resources such as WordNet.Therefore, it can be easily adapted to the broad set of languages for which corpora exist. We demonstrate the applicability of the technique by analyzing nine process model collections from practice, including over 27,000 labels and covering three di↵erent languages. The results of the evaluation show that our technique yields stable results and can reliably deal with ambiguous cases. In this way, this paper provides an important contribution to the field of automated quality assurance of conceptual models.

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“…SUGAR focus on generating only static UML models from requirements, following the Rational Unified Process (RUP) [10]. Priyanka [11] demonstrate how to generate UML diagrams from NL specifications, by using an algorithm and implementing a prototype tool called RAPID. Herchi [12] suggest an approach based on natural language processing (NLP) technique for extracting class diagrams from textual requirements.…”

Section: Introductionmentioning

confidence: 99%