Applications of natural language processing in software traceability: A systematic mapping study

Pauzi, Zaki; Capiluppi, Andrea

doi:10.1016/j.jss.2023.111616

Cited by 15 publications

(5 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Natural language processing (NLP) in software engineering Natural language processing in software development NLP technology can drastically improve software development tasks [39]. It can support bug categorization [40], development of more secure software [41], program decomposition [42], classifying commitments [43], programming and coding [44], writing coherent and factually correct readmes [45], model-driven engineering [46], deployment of design patterns [47] and traceability management [48]-Natural language processing in software requirements engineering NLP can support human-performed linguistic analysis in requirements engineering [49,50], such as identifying domain concepts [51][52][53][54], establishing traceability links [55], requirement classification [56,57], handling ambiguity [58,59], preference extraction form scenarios [60], classification of nonfunctional requirements [61], standardization of requirements in agile approaches [62] and requirement elicitation [63].…”

Section: Use Of Artificial Intelligence In Management Of Software Dev...mentioning

confidence: 99%

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature

Kokol

2024

Preprint

View full text Add to dashboard Cite

Artificial intelligence (AI) has witnessed an exponential increase in its use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software engineering problems. However, a comprehensive and holistic understanding of the current status is missing. To close the above gap, synthetic knowledge synthesis was used to induce a research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and five themes, namely natural language processing in software engineering, use of artificial intelligence in the management of software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n=2042), followed by the United States (n=1193), India (n=934), Germany (n=445), and Canada (n=381). A high percentage (n=47.4%) of papers were funded, showing a strong interest in this research topic. The convergence of AI and software engineering can significantly reduce needed resources, improve quality, increase user experience, and improve the well-being of software developers.

show abstract

Section: Use Of Artificial Intelligence In Management Of Software Dev...mentioning

confidence: 99%

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature

Kokol

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Natural language processing in software development NLP technology can drastically improve software development tasks [36]. It can support bug categorization [35], development of more secure software, program decomposition [37], classifying commitments [38], programming and coding [39], writing coherent and factually correct readmes [40], model-driven engineering [41], deployment of design patterns [42] and traceability management [43].…”

Section: Natural Language Processing (Nlp) In Software Engineeringmentioning

confidence: 99%

The Use of AI in Software Engineering: A Synthetic Knowledge Synthesis of the Recent Research Literature

Kokol

2024

Information

View full text Add to dashboard Cite

Artificial intelligence (AI) has witnessed an exponential increase in use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software-engineering problems. However, a comprehensive and holistic understanding of the current status needs to be included. To close the above gap, synthetic knowledge synthesis was used to induce the research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and 5 themes—namely, natural language processing in software engineering, use of artificial intelligence in the management of the software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n = 2042), followed by the United States (n = 1193), India (n = 934), Germany (n = 445), and Canada (n = 381). A high percentage (n = 47.4%) of papers were funded, showing the strong interest in this research topic. The convergence of AI and software engineering can significantly reduce the required resources, improve the quality, enhance the user experience, and improve the well-being of software developers.

show abstract

“…This model is focused on tracking and tracing the interactions and relationships between different elements within the supply chain, such as suppliers, manufacturers, distributors, and consumers. [67,68] Hybrid traceability This model combines elements of different traceability models to provide a comprehensive and integrated traceability system. [69][70][71] Recent studies have emphasized the role of information traceability in mitigating quality and safety issues, optimizing resource productivity, facilitating information retrieval for management practices, and improving supply chain by increasing trust between suppliers and consumers in the context of sustainable development [14,19,48].…”

Section: Physical Traceabilitymentioning

confidence: 99%

A Traceability Framework to Enable Circularity in the Built Environment

Davari,

Jaberi,

Yousfi

et al. 2023

Sustainability

View full text Add to dashboard Cite

The transition towards a Circular Economy (CE) has been receiving an increasing amount of attention in the built asset industry. One of the key aspects of CE is traceability, which can enable the identification and tracking of materials, products, and their associated value throughout their entire lifecycle. However, achieving traceability has been challenging in the built asset industry due to the complex nature of construction projects and a lack of awareness about the benefits of traceability in achieving the circularity of building products and materials. Despite recent studies and efforts, a limited number of frameworks or guidelines exist to support traceability in the built asset industry. In many cases, several of the existing traceability standards, strategies, and guidelines must be identified and framed to support development and implementation of theories and models applicable within the built asset domain. This paper proposes a traceability framework consisting of five key components covering: the main purposes of traceability enabling CE principles, the role of traceability across asset lifecycle stages, the type of data needed to support traceability, the value of collaboration and coordination among industry stakeholders, and key enablers and drivers of traceability from technological and organizational perspectives. The proposed framework developed in this paper contributes to the effort aimed at framing the knowledge domain of CE through the traceability of products and materials in the built environment.

show abstract

Applications of natural language processing in software traceability: A systematic mapping study

Cited by 15 publications

References 50 publications

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature

The Use of AI in Software Engineering: A Synthetic Knowledge Synthesis of the Recent Research Literature

A Traceability Framework to Enable Circularity in the Built Environment

Contact Info

Product

Resources

About