A Systematic Literature Review on Machine Learning for Automated Requirements Classification

Pérez-Verdejo, J. Manuel; Sánchez-García, Ángel J.; Ocharán-Hernández, Jorge Octavio

doi:10.1109/conisoft50191.2020.00014

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…Binkhonain and Zhao (2019) introduced ML algorithms in the requirements elicitation domain by dividing the 24 related articles into 3 sections: NLP techniques, ML algorithms, and evaluation. Perez-Verdejo et al (2020) applied topic models and visualization techniques to analyze ML-based requirement classification articles. Wong et al (2017) identified various software requirements elicitation methods, including manual, rule-based, and ML-based approaches.…”

Section: Related Workmentioning

confidence: 99%

Machine learning in requirements elicitation: a literature review

Cheligeer

Huang

et al. 2022

AIEDAM

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A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords derived from these research questions led to 975 records initially retrieved from 7 scientific search engines. Finally, 86 articles were selected for inclusion in the review. As the primary research finding, we identified 15 ML-based requirement elicitation tasks and classified them into four categories. Twelve different data sources for building a data-driven model are identified and classified in this literature review. In addition, we categorized the techniques for constructing ML-based requirement elicitation methods into five parts, which are Data Cleansing and Preprocessing, Textual Feature Extraction, Learning, Evaluation, and Tools. More specifically, 3 categories of preprocessing methods, 3 different feature extraction strategies, 12 different families of learning methods, 2 different evaluation strategies, and various off-the-shelf publicly available tools were identified. Furthermore, we discussed the limitations of the current studies and proposed eight potential directions for future research.

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Section: Related Workmentioning

confidence: 99%

Machine learning in requirements elicitation: a literature review

Cheligeer

Huang

et al. 2022

AIEDAM

View full text Add to dashboard Cite

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“…For instance, Abdelnabi et al (2021) [17] provided a comprehensive survey of methods for generating UML diagrams from natural language requirements, focusing on the automation and efficiency of these processes. Similarly, Mornie et al (2023) [18] and Perez-Verdejo et al (2020) [19] have systematically reviewed the application of NLP in visualizing UML models from user stories and classifying software requirements, respectively, highlighting the evolving challenges and the technologies employed [19].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review on Using Natural Language Processing in Software Requirements Engineering

Necula,

Dumitriu,

Greavu-Șerban

2024

Electronics

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This systematic literature review examines the integration of natural language processing (NLP) in software requirements engineering (SRE) from 1991 to 2023. Focusing on the enhancement of software requirement processes through technological innovation, this study spans an extensive array of scholarly articles, conference papers, and key journal and conference reports, including data from Scopus, IEEE Xplore, ACM Digital Library, and Clarivate. Our methodology employs both quantitative bibliometric tools, like keyword trend analysis and thematic mapping, and qualitative content analysis to provide a robust synthesis of current trends and future directions. Reported findings underscore the essential roles of advanced computational techniques like machine learning, deep learning, and large language models in refining and automating SRE tasks. This review highlights the progressive adoption of these technologies in response to the increasing complexity of software systems, emphasizing their significant potential to enhance the accuracy and efficiency of requirement engineering practices while also pointing to the challenges of integrating artificial intelligence (AI) and NLP into existing SRE workflows. The systematic exploration of both historical contributions and emerging trends offers new insights into the dynamic interplay between technological advances and their practical applications in SRE.

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The application of AI techniques in requirements classification: a systematic mapping

Kaur,

Kaur

2024

Artif Intell Rev

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Requirement Analysis is the essential sub-field of requirements engineering (RE). From the last decade, numerous automatic techniques are widely exploited in requirements analysis. In this context, requirements identification and classification is challenging for RE community, especially in context of large corpus and app review. As a consequence, several Artificial Intelligence (AI) techniques such as Machine learning (ML), Deep learning (DL) and transfer learning (TL)) have been proposed to reduce the manual efforts of requirement engineer. Although, these approaches reported promising results than traditional automated techniques, but the knowledge of their applicability in real-life and actual use of these approaches is yet incomplete. The main objective of this paper is to systematically investigate and better understand the role of Artificial Intelligence (AI) techniques in identification and classification of software requirements. This study conducted a systematic literature review (SLR) and collect the primary studies on the use of AI techniques in requirements classification. (1) this study found that 60 studies are published that adopted automated techniques in requirements classification. The reported results indicate that transfer learning based approaches extensively used in classification and yielding most accurate results and outperforms the other ML and DL techniques. (2) The data extraction process of SLR indicates that Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are widely used in selected studies. (3) Precision and Recall are the commonly used metrics for evaluating the performance of automated techniques. This paper revealed that while these AI approaches reported promising results in classification. The applicability of these existing techniques in complex and real-world settings has not been reported yet. This SLR calls for the urge for the close alliance between RE and AI techniques to handle the open issues confronted in the development of some real-world automated system.

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A Systematic Literature Review on Machine Learning for Automated Requirements Classification

Cited by 8 publications

References 24 publications

Machine learning in requirements elicitation: a literature review

Machine learning in requirements elicitation: a literature review

A Systematic Literature Review on Using Natural Language Processing in Software Requirements Engineering

The application of AI techniques in requirements classification: a systematic mapping

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