An automated object-based approach to transforming requirements to class diagrams

Dahhane, Walid; Zeaaraoui, Adil; Ettifouri, El Hassane; Bouchentouf, Toumi

doi:10.1109/icocs.2014.7060906

Cited by 13 publications

(6 citation statements)

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“…Some studies were authored/co-authored by the same person, indicating the existence of an active research group in this field. [33] Identify ambiguous user stories [34] Define and measure quality factors from user stories [4], [35] Obtain a security defect reporting form from user stories [36] Indicate duplication between user stories [37] Generate model/artifact Generate a test case from user stories [38]- [43] Generate a class diagram from user stories [44], [45] Generate a sequence diagram from user stories [46] Generate a use case diagram from user stories [47]- [49] Generate a use case scenario from user stories [50] Generate a multi-agent system from user stories [51] Generate a source code from user stories [40] Generate a BPMN diagram from user stories [40] Identify the key abstractions To understand the semantic connection in user stories [52]- [54] Identify topics and summarizing user stories [55], [56] Construct a goal model from a set of user stories. [57] Define ontology for user stories [58] Extract the conceptual model of user stories [59], [60] To find the linguistic structure of user stories [61] Prioritizing and estimation of user story complexity [62], [63] Extracting user stories from text [64]- [66] Trace links between model/NL requirements Tracking the development status of user stories from software artifacts [67] Identify the type of dependency of user stories [68] Traceability user stories and software artifact [69]…”

Section: Fig 4 Authorship Distribution Per Countrymentioning

confidence: 99%

“…The user story can be either input or output of the generated artifact. Fourteen studies reported methods for generating software model/artifacts from user stories, that is, generating a test case from user stories [35][36][37][38][39]64], generating class diagrams from user stories [40,41], generating sequence diagrams from user stories [46], generating a use case diagram from user stories [43][44][45], generating a use case scenario from user stories [50], generating a multi-agent system from user stories [51], generating a source code from user stories [40], and generating BPMN diagrams from user stories [40]. The software artifact generation aims to cut time and cost in software development and avoid inconsistencies, incompleteness, and incorrect requirements and artifact/software models.…”

Section: ) Generating the Model/artifactmentioning

confidence: 99%

“…This technique was used to generate use case diagrams [48], sequence diagrams [46], and use case scenarios [50] from user stories. The same approach was taken by [44], [45] to generate class diagrams. In generating use case diagrams, [47] categorized the aspects of what in user stories into three categories, namely task, capability, and goal, to produce more detailed use case diagrams involving <include> and <extend> as dependency relationships.…”

Section: ) Generating the Model/artifactmentioning

confidence: 99%

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User Stories and Natural Language Processing: A Systematic Literature Review

2021

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User stories have been widely accepted as artifacts to capture the user requirements in agile software development. They are short pieces of texts in a semi-structured format that express requirements. Natural language processing (NLP) techniques offer a potential advantage in user story applications. Objective: Conduct a systematic literature review to capture the current state-of-the-art of NLP research on user stories. Method: The search strategy is used to obtain relevant papers from SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar. Inclusion and exclusion criteria are applied to filter the search results. We also use the forward and backward snowballing techniques to obtain more comprehensive results. Results: The search results identified 718 papers published between January 2009 to December 2020. After applying the inclusion/exclusion criteria and the snowballing technique, we identified 38 primary studies that discuss NLP techniques in user stories. Most studies used NLP techniques to extract aspects of who, what, and why from user stories. The purpose of NLP studies in user stories is broad, ranging from discovering defects, generating software artifacts, identifying the key abstraction of user stories, and tracing links between model and user stories. Conclusion: NLP can help system analysts manage user stories. Implementing NLP in user stories has many opportunities and challenges. Considering the exploration of NLP techniques and rigorous evaluation methods is required to obtain quality research. As with NLP research in general, the ability to understand a sentence's context continues to be a challenge.

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Section: Fig 4 Authorship Distribution Per Countrymentioning

confidence: 99%

Section: ) Generating the Model/artifactmentioning

confidence: 99%

Section: ) Generating the Model/artifactmentioning

confidence: 99%

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User Stories and Natural Language Processing: A Systematic Literature Review

2021

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“…Literature suggests that different techniques such as SUGAR [16] and Object-oriented Analysis and Design Approach for Requirements Engineering (OOADA-RE) [17] have also been employed over the years for automatic extraction of use case diagrams/class diagrams. SUGAR can extract the use case diagram and class diagram from the functional requirement by formulating heuristic rules using NLP techniques whereas OOADA-RE [17] employs story cards as an intermediate level for the representation of requirement and then uses this intermediate level for automatic extraction of the class diagrams.…”

Section: Related Workmentioning

confidence: 99%

Automated class diagram elicitation using intermediate use case template

2021

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Class diagrams, being more illustrative, provide an easier way of understanding software requirements compared to use case descriptions. Both manual and automated methods are used for the extraction of class diagrams from requirements. The automated techniques employ certain extraction rules and natural language processing methods. Each use case description template introduces a small set of extraction rules. However, when all types of templates are considered, the number of rules becomes large and the procedure becomes tedious. Thus, researchers restricted the class diagram extraction procedure to some specific use case description templates. However, such a restriction negatively affects the software developers as they get restricted to working with only limited templates. The proposed work in this paper strives to remove this restriction on developers by introducing an intermediate template. The traditional use case description templates get transformed into the intermediate template and the rule extraction procedure is then applied to this intermediate template. This reduces the total number of extraction rules and hence, brings down the extraction complexity. The class diagrams extracted from use case description templates of different domains using proposed technique show more accuracy in terms of completeness and correctness when compared with the state of the art approaches.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Reviewing the state-of-the-art in creating conceptual models from user stories, we identify three research gaps. First, apart from Wautelet et al [22], studies focus on generating just one kind of conceptual model (e.g., use case model [18], [23], [24]; class diagram [25], [26]; goal model [27], [28]; OWL model [29]) whereas in software development different conceptual models are used to cover different perspectives (e.g., data, process, function, interaction) of the software system in development. Second, models are generated from user stories for a particular use (e.g., generating test cases [18], reasoning over requirements [30], obtaining a domain model [29]) or the intended use is implicit (e.g., [31]).…”

Section: Introductionmentioning

confidence: 99%