Intelligent Software Engineering: The Significance of Artificial Intelligence Techniques in Enhancing Software Development Lifecycle Processes

Kulkarni, Vaishnavi; Kolhe, Anurag; Kulkarni, Jay

doi:10.1007/978-3-030-96308-8_7

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…The academic community started to research and inject new AI-based approaches to provide solutions to traditional software engineering problems [5] and critical activities [6]. Examples include software testing [7], maintenance [8], requirements extraction [9], ambiguity resolution [10], software vulnerability detection [11], and software engineering education [12]. Despite the increasing prevalence of AI use in software engineering, a comprehensive and holistic understanding of the current status, possible target applications, practical software engineering usage scenarios, and unavoidable limitations, ethical concerns, and challenges remain unclear [6].…”

Section: Introductionmentioning

confidence: 99%

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

Kokol

2024

Preprint

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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.

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Section: Introductionmentioning

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

“…The academic community started to research and inject new AI-based approaches to provide solutions to traditional software-engineering (SE) problems [5] and critical activities [6]. Examples include software testing [7], maintenance [8], requirements extraction [9], ambiguity resolution [10], software vulnerability detection [11], and software-engineering education [11]. Due to the increasing prevalence of AI use in software engineering, some reviews on the use of AI in software engineering have already been performed.…”

Section: Introductionmentioning

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.

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A Model of a Parallel Design Environment for the Development of Decision-Making IoT Systems

Łuczak

Stróżański

Orłowski

2022

Transactions on Computational Collective Intelligence XXXVII

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Intelligent Software Engineering: The Significance of Artificial Intelligence Techniques in Enhancing Software Development Lifecycle Processes

Cited by 4 publications

References 25 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 Model of a Parallel Design Environment for the Development of Decision-Making IoT Systems

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