Generating Use Case Scenarios from User Stories

Gilson, Fabian; Galster, Matthias

doi:10.1145/3379177.3388895

Cited by 16 publications

(22 citation statements)

References 40 publications

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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%

“…The main approach commonly used was to employ part-of-speech tagging to identify verbs and nouns as elements in UML diagrams. 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.…”

Section: ) Generating the Model/artifactmentioning

confidence: 99%

“…This category aims to identify the key abstractions from NL documents that help analysts understand unknown domains. The key abstraction identification was performed by 16 studies to understand the semantic connection in user stories [48][49][50], identify topics and summarizing user stories [55], [56], construct a goal model from a set of user stories [57], define the ontology for user stories [58], extract the conceptual model of user stories [53,54], prioritize and estimate the user story complexity [56,57], find the linguistic structure of user stories [61], and extract user stories from text [64]- [66].…”

Section: ) Identifying the Key Abstractionsmentioning

confidence: 99%

“…Several methods can be used to obtain and understand the semantic connections in user stories [48][49][50] using the semantic similarity from user stories. Barbosa et al [52] utilized the cosine similarity function and clustering using the K-medoids algorithm.…”

Section: ) Identifying the Key Abstractionsmentioning

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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