Relative Extraction Methodology for class diagram generation using dependency graph

Krishnan, Hema; Samuel, Philip

doi:10.1109/icccct.2010.5670730

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…8) Dataset: Most of the primary studies applied their models to one or more NL software requirement text(s) (aka casestudy) to demonstrate the models' performance. We found input NL case-study appeared more than twice are: Library [12], [15], [16], [24], [31], [36], [64], ATM [37], [45], [60], [76], Elevator [45], [46], [60], Banking [22], [25], [26], [36], Home [53], [70], Arena [45], [60]. However, some studies didn't even address their input case-study [6], [14], [15], [40], [66], [76].…”

Section: ) Formal Expression Extractionmentioning

confidence: 99%

“…Studies precision [6], [15], [16], [24], [25], [28], [29], [31], [36], [38], [40], [42], [53], [54], [63], [70], [76]- [78] recall [6], [15], [16], [24], [25], [28], [29], [31], [36], [38], [40], [53], [63], [70], [76]- [78] enumeration [22], [25], [32], [57], [76] accuracy [14], [40], [41], [45], [59] relationship [4], [6], [29], [70] false positive [36], [53], [78] over specification …”

Section: Metricsmentioning

confidence: 99%

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Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

Ahmed,

Eisty

2022

Preprint

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Context: Processing Software Requirement Specifications (SRS) manually takes a much longer time for requirement analysts in software engineering. Researchers have been working on making an automatic approach to ease this task. Most of the existing approaches require some intervention from an analyst or are challenging to use. Some automatic and semi-automatic approaches were developed based on heuristic rules or machine learning algorithms. However, there are various constraints to the existing approaches of UML generation, such as restriction on ambiguity, length or structure, anaphora, incompleteness, atomicity of input text, requirements of domain ontology, etc. . Objective: This study aims to better understand the effectiveness of existing systems and provide a conceptual framework with further improvement guidelines. Method: We performed a systematic literature review (SLR). We conducted our study selection into two phases and selected 70 papers. We conducted quantitative and qualitative analyses by manually extracting information, cross-checking, and validating our findings. Result: We described the existing approaches and revealed the issues observed in these works. We identified and clustered both the limitations and benefits of selected articles. Conclusion: This research upholds the necessity of a common dataset and evaluation framework to extend the research consistently. It also describes the significance of natural language processing obstacles researchers face. In addition, it creates a path forward for future research.

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Section: ) Formal Expression Extractionmentioning

confidence: 99%

Section: Metricsmentioning

confidence: 99%

Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

Ahmed,

Eisty

2022

Preprint

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“…The target is models and UML diagrams, for instance, creation of use case diagrams [21] and UML Activity Diagrams using identification of simple verbal sentences [22] from textual requirements in Arabic. UML class diagrams from textual requirements [23], and from use case descriptions [24] are created in English. Researchers also analyse textual user requirements in natural language and requirements used by engineering diagrams to create the Use Case Path model, the Hybrid Activity Diagram model and the Domain model [25].…”

Section: Related Workmentioning

confidence: 99%

Extracting TFM Core Elements From Use Case Scenarios by Processing Structure and Text in Natural Language

Nazaruka

Osis

Gribermane

2019

Applied Computer Systems

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Extracting core elements of Topological Functioning Model (TFM) from use case scenarios requires processing of both structure and natural language constructs in use case step descriptions. The processing steps are discussed in the present paper. Analysis of natural language constructs is based on outcomes provided by Stanford CoreNLP. Stanford CoreNLP is the Natural Language Processing pipeline that allows analysing text at paragraph, sentence and word levels. The proposed technique allows extracting actions, objects, results, preconditions, post-conditions and executors of the functional features, as well as cause-effect relations between them. However, accuracy of it is dependent on the used language constructs and accuracy of specification of event flows. The analysis of the results allows concluding that even use case specifications require the use of rigor, or even uniform, structure of paths and sentences as well as awareness of the possible parsing errors.

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Using Stanford CoreNLP Capabilities for Semantic Information Extraction from Textual Descriptions

Nazaruka

Osis

Griberman

2020

Communications in Computer and Information Science

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Relative Extraction Methodology for class diagram generation using dependency graph

Cited by 10 publications

References 7 publications

Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

Extracting TFM Core Elements From Use Case Scenarios by Processing Structure and Text in Natural Language

Using Stanford CoreNLP Capabilities for Semantic Information Extraction from Textual Descriptions

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