An Innovative Approach for Generating Static UML Models from Natural Language Requirements

Deeptimahanti, Deva Kumar; Sanyal, Ratna

doi:10.1007/978-3-642-10242-4_13

Cited by 17 publications

(12 citation statements)

References 2 publications

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“…3. We have proposed eight syntactic reconstruction rules [9][10][11] to handle large NL SRS and normalize complex requirements into simple ones to reduce their ambiguity. It can also handle compound word morphological analysis where WordNet [29] fails, identified 247 determiners which are specific for requirements engineering.…”

Section: Contributions Of Umgarmentioning

confidence: 99%

“…Since, there is no tool available to perform automatic syntactic reconstruction of NL sentences; we have defined eight syntactic reconstructing rules that have been implemented in UMGAR [9][10][11] UMGAR scans each sentence to test whether that requirement satisfies the Statement sentence structure which is of the form "Subject: Predicate" or "Subject: Predicate: Object", and applies rules accordingly. Subject and object are usually represented as a noun phrase (denoted by NP) and a predicate as verb phrase (denoted as VP).…”

Section: Syntactic Reconstructionmentioning

confidence: 99%

“…Candidate Using proposed syntactic reconstruction rules [9][10][11], all these 26 requirements are normalized automatically into 33 simple sentences, so that each requirement can be of the form "SubjectPredicate" or "Subject-Predicate-Object". Using third reconstruction rule [10], compound requirement -"Candidate can provide information about his academic, work experience and referee details" can be reconstructed as "Candidate can provide information about his academic details.…”

Section: Using Umgarmentioning

confidence: 99%

“…The diagrams visualized on canvas have problems regarding editing and updating as we observed in SUGAR [9,10]. In order to avoid such problems, UMGAR provides an efficient XMI parser to support the visualization and manipulation of the generated models in any modeling tool, which has XMI import feature.…”

Section: Xmi Parsermentioning

confidence: 99%

“…Our previous work, Static UML model Generator from Analysis of Requirements (SUGAR), [9,10] just focused on generating static UML models such as use-case and analysis class models from NL requirements. This paper is an extension to our earlier work [9][10][11] describing a technique named as UML Model Generator from analysis of Requirements (UMGAR), to provide semi-automated support for developing both static and dynamic UML models from NL requirements and evaluated with the help of a case study. Our technique exploits three efficient NLP techniques for parsing sentences and performing various analyses such as pronoun resolution, morphological analysis.…”

Section: Introductionmentioning

confidence: 99%

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Semi-automatic generation of UML models from natural language requirements

Deeptimahanti

Sanyal

2011

Proceedings of the 4th India Software Engineering Conference

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Going from requirements analysis to design phase is considered as one of the most complex and difficult activities in software development. Errors caused during this activity can be quite expensive to fix in later phases of software development. One main reason for such potential problems is due to the specification of software requirements in Natural Language format. To overcome some of these defects we have proposed a technique, which aims to provide semi-automated assistance for developers to generate UML models from normalized natural language requirements using Natural Language Processing techniques. This technique initially focuses on generating use-case diagram and analysis class model (conceptual model) followed by collaboration model generation for each use-case. Then it generates a consolidated design class model from which code model can also be generated. It also provides requirement traceability both at design and code levels by using Key-Word-InContext and Concept Location techniques respectively to identify inconsistencies in requirements. Finally, this technique generates XML Metadata Interchange (XMI) files for visualizing generated models in any UML modeling tool having XMI import feature. This paper is an extension to our existing work by enhancing its complete usage with the help of Qualification Verification System as a case study.

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Section: Contributions Of Umgarmentioning

confidence: 99%

Section: Syntactic Reconstructionmentioning

confidence: 99%