Latent semantic centrality based automated requirements prioritization

Misra, Janardan; Sengupta, Shubhahsis; Das, Subhabrata

doi:10.1145/2590748.2590753

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…In the context of comparing and analysing requirements, the work of J. Misra is significant. He used LSA to prioritize requirements within clusters (Misra et al, 2014). Although this helps engineers to prioritize requirements, it does not provide an understanding of the interactions between requirements.…”

Section: Natural Language Processing and Kgmentioning

confidence: 99%

A prospective analysis of the engineering design discipline evolution based on key influencing trends

Coatanéa

Nagarajan

Panicker

et al. 2023

JID

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Design and manufacturing sectors are vital agents of an economy. However, multiple challenges influence product designs such as the predicted scarcity of energy and primary materials, the ubiquitous integration of electronic components and computing science in systems’ architectures, the pervasive production of data by most systems, the emphasis given to CO2 free energy solutions, recycling, and reuse, the transformation of the consumption model from product ownership to product as a service, as well as the geopolitical conflicts. Major technological advancements leading to transformation in socio-economic practices would be required to address these challenges which can have a profound effect on design and manufacturing activities. This research aims to evaluate the potential impact and modification induced by such transformations on product design process. The research identifies that early design automation can enable coping with unmanageable cognitive load generated by cascading changes. A list of modifications to current design practices is proposed to enable the development of a new generation of design tools. The article provides an initial prospective effort to discuss the potential services and functionality that will be offered by future design tools’.

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Section: Natural Language Processing and Kgmentioning

confidence: 99%

A prospective analysis of the engineering design discipline evolution based on key influencing trends

Coatanéa

Nagarajan

Panicker

et al. 2023

JID

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“…Then, each requirement is represented as a vector representation in this k-dimensional space. In most related papers, this representation is employed to calculate the similarity between requirements as a part of similarity-based rules [70], [107] or clustering machine learning techniques [78], [99], [135].…”

Section: ) Ontology-based Representationmentioning

confidence: 99%

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

2022

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Natural Language Processing (NLP) is widely used to support the automation of different Requirements Engineering (RE) tasks. Most of the proposed approaches start with various NLP steps that analyze requirements statements, extract their linguistic information, and convert them to easy-toprocess representations, such as lists of features or embedding-based vector representations. These NLPbased representations are usually used at a later stage as inputs for machine learning techniques or rulebased methods. Thus, requirements representations play a major role in determining the accuracy of different approaches. In this paper, we conducted a survey in the form of a systematic literature mapping (classification) to find out (1) what are the representations used in RE tasks literature, (2) what is the main focus of these works, (3) what are the main research directions in this domain, and ( 4) what are the gaps and potential future directions. After compiling an initial pool of 2,227 papers, and applying a set of inclusion/exclusion criteria, we obtained a final pool containing 104 relevant papers. Our survey shows that the research direction has changed from the use of lexical and syntactic features to the use of advanced embedding techniques, especially in the last two years. Using advanced embedding representations has proved its effectiveness in most RE tasks (such as requirement analysis, extracting requirements from reviews and forums, and semantic-level quality tasks). However, representations that are based on lexical and syntactic features are still more appropriate for other RE tasks (such as modeling and syntax-level quality tasks) since they provide the required information for the rules and regular expressions used when handling these tasks. In addition, we identify four gaps in the existing literature, why they matter, and how future research can begin to address them.

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“…• Requirements Prioritization: the main target for this task is to determine which candidate requirements of a software product should be included in a certain release. We recognized 5 papers handling this task [132,133,134,135,136].…”

Section: Requirements Managementmentioning

confidence: 99%

“…Then, each requirement is represented as a vector representation in this k-dimensional space. In most related papers, this representation is employed to calculate the similarity between requirements as a part of similarity-based rules [70,107] or clustering machine learning techniques [135,99,78].…”

Section: Topic Modeling Based Representationsmentioning

confidence: 99%

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

Sonbol,

Rebdawi,

Ghneim

2022

Preprint

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Natural Language Processing (NLP) is widely used to support the automation of different Requirements Engineering (RE) tasks. Most of the proposed approaches start with various NLP steps that analyze requirements statements, extract their linguistic information, and convert them to easy-toprocess representations, such as lists of features or embedding-based vector representations. These NLP-based representations are usually used at a later stage as inputs for machine learning techniques or rule-based methods. Thus, requirements representations play a major role in determining the accuracy of different approaches. In this paper, we conducted a survey in the form of a systematic literature mapping (classification) to find out ( 1) what are the representations used in RE tasks literature, ( 2) what is the main focus of these works, (3) what are the main research directions in this domain, and ( 4) what are the gaps and potential future directions. After compiling an initial pool of 2,227 papers, and applying a set of inclusion/exclusion criteria, we obtained a final pool containing 104 relevant papers. Our survey shows that the research direction has changed from the use of lexical and syntactic features to the use of advanced embedding techniques, especially in the last two years. Using advanced embedding representations has proved its effectiveness in most RE tasks (such as requirement analysis, extracting requirements from reviews and forums, and semantic-level quality tasks). However, representations that are based on lexical and syntactic features are still more appropriate for other RE tasks (such as modeling and syntax-level quality tasks) since they provide the required information for the rules and regular expressions used when handling these tasks. In addition, we identify four gaps in the existing literature, why they matter, and how future research can begin to address them.

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Latent semantic centrality based automated requirements prioritization

Cited by 6 publications

References 21 publications

A prospective analysis of the engineering design discipline evolution based on key influencing trends

A prospective analysis of the engineering design discipline evolution based on key influencing trends

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

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