Identification and Classification of Architecturally Significant Functional Requirements

Chatterjee, Ranit; Ahmed, Abdul Wahab; Anish, Preethu Rose

doi:10.1109/aire51212.2020.00008

Cited by 11 publications

(6 citation statements)

References 31 publications

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“…We recognized 21 papers proposing solutions to various requirements classification tasks. Most of them focused on Functional/Non-Functional classification tasks [10], [11], [39]- [49], while the remaining focused on other classification tasks: security/Not security [50]- [52], topic-based classification [53], and classification based on requirements importance level [54].…”

Section: ) Requirements Analysismentioning

confidence: 99%

“…Aggregation-based [11], [47], [56], [58], [59], [64], [66], [76], [84], [91], [98], [101], [104], [128] RNN-based [43], [51], [54], [67], [112], [137] CNN-based [39], [50] TABLE 3. the used statement embedding techniques with their related paper…”

Section: Related Papersmentioning

confidence: 99%

“…It finds a dense and low-dimensional semantic representation for each requirement statement by sequentially and recurrently processing its words. Many RNN architectures have been used in the related papers such as LSTM [43], [51], [137], Bi-LSTM [54], BI-GRU [67], and Skip-Thought [112].…”

Section: Related Papersmentioning

confidence: 99%

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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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Section: ) Requirements Analysismentioning

confidence: 99%

Section: Related Papersmentioning

confidence: 99%

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

2022

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“…We recognized 21 papers proposing solutions to various requirements classification tasks. Most of them focused on Functional/Non-Functional classification tasks [39,40,41,10,11,42,43,44,45,46,47,48,49], while the remaining focused on other classification tasks: security/Not security [50,51,52], topic-based classification [53], and classification based on requirements importance level [54].…”

Section: Requirements Analysismentioning

confidence: 99%

“…It finds a dense and low-dimensional semantic representation for each requirement statement by sequentially and recurrently processing its words. Many RNN architectures have been used in the related papers such as LSTM [51,137,43], Bi-LSTM [54], BI-GRU [67], and Skip-Thought [112].…”

Section: Wordmentioning

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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Automatic derivation of use case diagrams from interrelated natural language requirements

Schleifer,

Lungu,

Kruse

et al. 2024

Proc. Des. Soc.

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Transferring natural language requirements to use case diagrams helps to avoid inherent ambiguities. However, this is usually a manual, time-consuming task that can be accelerated by utilizing Artificial Intelligence in terms of Natural Language Processing. Thus, this contribution proposes a conceptual framework for automatically grouping interrelated functional requirements and deriving use case diagrams by combining formerly isolated approaches. Moreover, the latter are evaluated by a qualitative potential analysis to support their future industrial application.

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Identification and Classification of Architecturally Significant Functional Requirements

Cited by 11 publications

References 31 publications

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

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

Automatic derivation of use case diagrams from interrelated natural language requirements

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