Examining deep learning’s capability to spot code smells: a systematic literature review

Malhotra, Ruchika; Jain, Bhawna; Kessentini, Marouane

doi:10.1007/s10586-023-04144-1

Cited by 1 publication

(1 citation statement)

References 60 publications

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“…In the same context, deep learning is also used to detect code vulnerability [89,90]-Technical depth and code smell detection Machine learning has recently been often used to detect self-admitted technical depth [91][92][93] or technical debt in general [94,95]. Technical debt is closely linked to code smells and anti-patterns, which are spotted [96,97], classified [98,99], or identified [100]with artificial intelligence techniques.…”

Section: Deep Learning In Empirical Software Engineering Focusing On ...mentioning

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: Deep Learning In Empirical Software Engineering Focusing On ...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

Examining deep learning’s capability to spot code smells: a systematic literature review

Cited by 1 publication

References 60 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

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