Comparative Analysis for Predicting Non-Functional Requirements using Supervised Machine Learning

Khatian, Vajeeha Mir; Arain, Qasim Ali; Alenezi, Mamdouh; Raza, Muhammad Owais; Shaikh, Fariha; Farah, Isma

doi:10.1109/caida51941.2021.9425236

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…requirement analysis, requirements extraction, modelling). The main focus of the examined papers lies on the software area and the respective distinctions of the requirements into functional and non-functional and thereby on the specific implementation and the used representations and frameworks, e.g., described in Hey et al (2020), Mir Khatian et al (2021 and Rahman et al (2019). Zhao et al (2022) also provide a comprehensive review of the applications of NLP in RE research, focusing on the state of the literature and research and the NLP technologies used.…”

Section: Related Workmentioning

confidence: 99%

Natural Language Processing in Requirements Engineering and Its Challenges for Requirements Modelling in the Engineering Design Domain

van Remmen,

Horber,

Lungu

et al. 2023

Proc. Des. Soc.

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Requirements represent a central element in product development. The large number of requirements inevitably results in an increased susceptibility to errors, an expenditure of time and development costs. The associated problems motivate the application of Artificial Intelligence in the form of Natural Language Processing (NLP). In Requirements Engineering one main task is the classification of requirements which serves as the input in architectural models e.g. in SysML. In mechanical engineering there is still little overview regarding the interface between requirements classification and modelling. This paper provides an overview of the requirement classes and entities used in the literature and analyses their utilisation in modelling. Existing requirements classes usually do not offer the flexibility to be transferred to other domains. However, basic structures can be adopted from those classifications. This enables a clear assignment of existing classes to object classes in modelling. Resulting from the conducted literature study the observed predominant focus of research on the software industry requires an extension of the existing requirement classes and entities to enable further use and transfer to mechanical engineering.

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Section: Related Workmentioning

confidence: 99%

Natural Language Processing in Requirements Engineering and Its Challenges for Requirements Modelling in the Engineering Design Domain

van Remmen,

Horber,

Lungu

et al. 2023

Proc. Des. Soc.

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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%

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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“…The automatic classification of NFR offers several benefits, including a reduction in stakeholder effort, decreased stakeholder involvement, real-time classification, and simplified identification of stakeholders with expertise, among others. The automatic classification of non-functional requirements has been extensively studied Binkhonain and Zhao [2019], Tóth and Vidács [2018b,a], , Khatian et al [2021]. As requirements are expressed in natural language, several NLP techniques have been used in this process in the last years with promising results.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Non-Functional Requirements Classification for Spanish Text: Traditional vs. Deep Learning Approaches

Limaylla-Lunarejo,

Condori-Fernandez,

Luaces

2023

LatinX in AI at Neural Information Processing Systems Conference 2023

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The automatic classification of non-functional requirements helps to reduce time and effort for the stakeholders. Several techniques have been used for this task, including the latest techniques in Machine Learning (ML) and Natural Language Processing (NLP), such as pre-trained models, with promising results. This research aims to analyze the performance metrics to classify requirements into sub-classes of non-functional type. Six distinct algorithms, including both traditional machine learning (ML) and deep learning (DL), were trained using a Spanish-translated PROMISE NFR dataset to assess and compare their performance outcomes. The findings reveal that Support Vector Machine (SVM) with Bag of Word technique, and fastText overperformed the other algorithms. However, fastText stands out between the two due the ease of implementation and the absence of data preprocessing.

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Comparative Analysis for Predicting Non-Functional Requirements using Supervised Machine Learning

Cited by 5 publications

References 20 publications

Natural Language Processing in Requirements Engineering and Its Challenges for Requirements Modelling in the Engineering Design Domain

Natural Language Processing in Requirements Engineering and Its Challenges for Requirements Modelling in the Engineering Design Domain

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

Evaluating Non-Functional Requirements Classification for Spanish Text: Traditional vs. Deep Learning Approaches

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