ClassSum: a deep learning model for class-level code summarization

Li, Mingchen; Yu, Huiqun; Fan, Guisheng; Zhou, Ziyi; Huang, Jiawen

doi:10.1007/s00521-022-07877-z

Cited by 7 publications

(2 citation statements)

References 45 publications

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“…For example, Hybrid-DeepCom and DeepComenter tools automatically generate code comments for Java functional units [84,85]. Similarly, deep learning is also used to generate pseudo-code from program code [86], classify code according to readability [87], or summarise code [88]. 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].…”

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

“…For example, the Hybrid-DeepCom and DeepComenter tools automatically generate code comments for Java functional units [75,76]. Similarly, deep learning is also used to generate pseudo-code from program code [77], classify code according to readability [78], or summarize code [79]. In the same context, deep learning is also used to detect code vulnerability [80,81].…”

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

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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Key-based data augmentation with curriculum learning for few-shot code search

Zhang,

Peng,

et al. 2024

Neural Comput & Applic

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ClassSum: a deep learning model for class-level code summarization

Cited by 7 publications

References 45 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

Key-based data augmentation with curriculum learning for few-shot code search

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