Automated Goal Model Extraction from User Stories Using NLP

Gunes, Tugce; Aydemir, Fatma Başak

doi:10.1109/re48521.2020.00052

Cited by 19 publications

(15 citation statements)

References 26 publications

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“…Trkman et al [13] experiment with human experts to investigate the effectiveness of conceptual modelsspecifically business process models-compared to textual requirements and point out that participants achieve equal or better results when a BPMN model is provided. Günes ¸and Aydemir [14], propose a method for capturing relations among user stories via automatically generating goal models using an NLP pipeline to eliminate the human effort required for constructing models. Wautelet et al [15] state that due to its simple structure a large number of user stories are needed in large-scale projects.…”

Section: A Agile Requirements Engineeringmentioning

confidence: 99%

A Framework To Improve User Story Sets Through Collaboration

Köse¹,

Aydemir²

2023

Preprint

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Agile methodologies have become increasingly popular in recent years. Due to its inherent nature, agile methodologies involve stakeholders with a wide range of expertise and require interaction between them, relying on collaboration and customer involvement. Hence, agile methodologies encourage collaboration between all team members so that more efficient and effective processes are maintained. Generating requirements can be challenging, as it requires the participation of multiple stakeholders who describe various aspects of the project and possess a shared understanding of essential concepts. One simple method for capturing requirements using natural language is through user stories, which document the agreed-upon properties of a project. Stakeholders try to strive for completeness while generating user stories, but the final user story set may still be flawed. To address this issue, we propose SCOUT: Supporting Completeness of User Story Sets, which employs a natural language processing pipeline to extract key concepts from user stories and construct a knowledge graph by connecting related terms. The knowledge graph and different heuristics are then utilized to enhance the quality and completeness of the user story sets by generating suggestions for the stakeholders. We perform a user study to evaluate SCOUT and demonstrate its performance in constructing user stories. The quantitative and qualitative results indicate that SCOUT significantly enhance the quality and completeness of the user story sets. Our contribution is threefold. First, we develop heuristics to suggest new concepts to include in user stories by considering both the individuals' and other team members' contributions. Second, we implement an open-source collaborative tool to support writing user stories and ensuring their quality. Third, we share the experimental setup and materials.

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Section: A Agile Requirements Engineeringmentioning

confidence: 99%

A Framework To Improve User Story Sets Through Collaboration

Köse¹,

Aydemir²

2023

Preprint

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“…[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%

“…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%

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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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“…The improvement of modeling language includes developing i* language and notation [9] [10], User Requirements Notation (URN) [11], Goal-oriented Requirements Language (GRL) [12], GORE modeling development uses Systems Modeling Language (SysML) [13], and Unified Modeling Language (UML) [14]. The improvement of approach or model on GORE includes Goal Model for Dynamic Systems (GmoDS) [15], Goal-Oriented Requirements Engineering framework for Systems of Systems (SoSGORE) [16], Agent Goal Model [17], Goal Dependency Model with Objects (GDMO) [18], trust-aware goal modeling [19], and realistic goal models [20].…”

Section: B Rq2: What Kind Of Contribution Given Through the Gore Research Done?mentioning

confidence: 99%

Goal-Oriented Requirements Engineering: State of the Art and Research Trend

2021

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The Goal-Oriented Requirements Engineering (GORE) is one approach that is widely used for the early stages of software development. This method continues to develop in the last three decades. In this paper, a literature study is conducted to determine the GORE state of the art. The study begins with a Systematic Literature Review (SLR) was conducted to determine the research trend in the last five years. This study reviewed 126 papers published from 2016 to 2020. The research continues with the author's search for scientific articles about GORE. There are 26 authors who actively publish GORE research results. Twenty-six authors were grouped into seven groups based on their relation or co-authoring scientific articles. An in-depth study of each group resulted in a holistic mapping of GORE research. Based on the analysis, it is known that most research focuses on improving GORE for an automated and reliable RE process, developing new models/frameworks/methods originating from GORE, and implementing GORE for the RE process. This paper contributes to a holistic mapping of the GORE approach. Through this study, it is known the various studies that are being carried out and research opportunities to increase automation in the entire RE process.

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Automated Goal Model Extraction from User Stories Using NLP

Cited by 19 publications

References 26 publications

A Framework To Improve User Story Sets Through Collaboration

A Framework To Improve User Story Sets Through Collaboration

User Stories and Natural Language Processing: A Systematic Literature Review

Goal-Oriented Requirements Engineering: State of the Art and Research Trend

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