Supporting Agile Software Development by Natural Language Processing

Plank, Barbara; Sauer, Thomas; Schaefer, Ina

doi:10.1007/978-3-642-45260-4_7

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…Other artifacts related to a particular methodology could also enhance the performance of the developed tool. For example, if the Scrum context is considered, we could consider the sprints and the backlog information [22] to enhance the quality of the data used to generate or transform artifacts. More studies may be needed to explore the impact of context with NLP.…”

Section: Approaches To Improve the Agile Documentation Process Using ...mentioning

confidence: 99%

“…NLP has been used to process requirements, gathering artifacts such as use cases [19] and user stories [20]; in fact, there is a preference for using NLP to process user stories [21]. This preference may be due to the user stories' characteristics [22], i.e., simple texts which are structured so that the most important elements of each requirement can be identified and their quality can be checked [23]. User stories are a method of representing requirements using a simple structure such as "Who?…”

Section: Introductionmentioning

confidence: 99%

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Agile Development Methodologies and Natural Language Processing: A Mapping Review

et al. 2022

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Agile software development is one of the most important development paradigms these days. However, there are still some challenges to consider to reduce problems during the documentation process. Some assistive methods have been created to support developers in their documentation activities. In this regard, Natural Language Processing (NLP) can be used to create various related tools (such as assistants) to help with the documentation process. This paper presents the current state-of-the-art NLP techniques used in the agile development documentation process. A mapping review was done to complete the objective, the search strategy is used to obtain relevant studies from ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Willey. The search results after inclusion and exclusion criteria application left 47 relevant papers identified. These papers were analyzed to obtain the most used NLP techniques and NLP toolkits. The toolkits were also classified by the kind of techniques that are available in each of them. In addition, the behavior of the research area over time was analyzed using the relevant paper found by year. We found that performance measuring methods are not standardized, and, in consequence, the works are not easily comparable. In general, the number of related works and its distribution per year shows a growing trend of the works related to this topic in recent years; this indicates that the adoption of NLP techniques to improve agile methodologies is increasing.

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Section: Approaches To Improve the Agile Documentation Process Using ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Agile Development Methodologies and Natural Language Processing: A Mapping Review

et al. 2022

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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%

“…Tracing the relationship between these models and NL requirements can assist during the software development process, particularly in inconsistency checking and change management [72]. Three studies focused on tracing the relationship between models and user stories: Plank et al [67] tracked the development status of user stories from software artifacts; Soni and Gaur [68] identified the dependency type of user stories, and Lucassen et al [69] tracked the traceability of user stories and software artifacts.…”

Section: ) Tracing Links Between Model/nl Requirementsmentioning

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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