Natural Language Processing (NLP) for Requirements Engineering: A Systematic Mapping Study

Zhao, Liping; Alhoshan, Waad; Ferrari, Alessio; Letsholo, Keletso J.; Ajagbe, Muideen A.; Chioasca, Erol-Valeriu; Batista-Navarro, Riza Theresa

doi:10.48550/arxiv.2004.01099

Cited by 10 publications

(15 citation statements)

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“…The close relationship between the fields of NLP and RE leads to the field of NLP4RE emerging. Zhao et al [29] define NLP4RE as "an area of research and development that seeks to apply NLP techniques requirements artifacts". There are numerous studies in the literature in the area of NLP4RE.…”

Section: Nlp For Requirements Engineeringmentioning

confidence: 99%

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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: Nlp For Requirements Engineeringmentioning

confidence: 99%

“…Due to stemming's reliance on vocabulary and morphological aspects of words, we prefer lemmatization. Stop-word removal is widely used in NLP tasks [29]. Nevertheless, following several test runs, we decide against removing stop words from the user stories.…”

Section: Noun Phrase Extraction Modulementioning

confidence: 99%

A Framework To Improve User Story Sets Through Collaboration

Köse¹,

Aydemir²

2023

Preprint

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“…NLP has been applied also in conceptual modeling [16], in requirement engineering [17,18], detecting domain specific ambiguity [19], and inspect medical records [20,21]. Most of these works rely on a semi-structured input, which is an impractical for manipulating a large amount of free written natural language written policy.…”

Section: Related Workmentioning

confidence: 99%

Converting natural language policy article into MBSE model

Chen

Bhada

2022

INCOSE International Symp

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System Engineers work on designing human interfaces for easy access to governance and technology. As we move to digital engineering, the policy documents are still written in natural language that sometimes is obscure and verbose. One way of digitizing is to convert natural language policies into machine readable system engineering models. In the past, converting natural language written policy documents into machine readable models involves great human effort and expert knowledge in relevant domains, which is a time consuming, tedious, and sometimes impossible task. Artificial Intelligence and its application have shown the potential to accelerate the process. In this study, we proposed a natural language processing (NLP)-based framework for information extraction under the general condition that can automatically detect the actors and their responsible actions. To validate the performance of the model developed, we compared the NLP generated report with manually created SysML model. The result shows that the precision and recall rate of extracting roles and responsibility is ~0.86 and ~0.66, respectively, representing that this text-to-model framework has the potential to accurately convert general policy documents into SysML.

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“…NLP explores how computers can be used to understand and manipulate natural language text or speech to do useful things [15]. Many disciplines have used and are increasingly using NLP with different purposes, and RE is one of them [16] [17]. NLP techniques in RE research are complemented with machine learning (ML), which allows learning from data (in different variations, which may involve the human in the loop, e.g.…”

Section: Explicit Feedback Analysismentioning

confidence: 99%

Data-Driven Requirements Engineering: A Guided Tour

Franch

2021

Communications in Computer and Information Science

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Data-driven approaches are becoming dominant in almost every single software engineering activity, and requirements engineering is not the exception. The analysis of data coming from several sources may indeed become an extremely useful input to requirements elicitation and management. However, benefits do not come for free. Techniques such as natural language processing and machine learning are difficult to master and require high-quality data and specific competences from different fields, whilst their generalization remains as a challenge. This paper introduces the main concepts behind data-driven requirements engineering, provides an overview of the state of the art in the field and identifies the main challenges to be addressed.

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Natural Language Processing (NLP) for Requirements Engineering: A Systematic Mapping Study

Cited by 10 publications

References 0 publications

A Framework To Improve User Story Sets Through Collaboration

A Framework To Improve User Story Sets Through Collaboration

Converting natural language policy article into MBSE model

Data-Driven Requirements Engineering: A Guided Tour

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