An adaptive 3D virtual learning environment for training software developers in scrum

Scott, Ezequiel; Campo, Marcelo

doi:10.1080/10494820.2021.1999985

Cited by 8 publications

(2 citation statements)

References 43 publications

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“…Machine learning has recently been often used to detect self-admitted technical depth [72,82,83] or technical debt in general [74,84]. Technical debt is closely linked to code smells and anti-patterns, which are spotted [85,86], classified [87,88], or identified [89] with artificial intelligence techniques.…”

Section: Technical Depth and Code Smell Detectionmentioning

confidence: 99%

“…Collaborative software development eco-systems where AI partners with human developers take team dynamics and self-organization into account [98], enabling significant improvements in productivity, code quality, and developer empowerment. • Adaptive continuous-learning platforms for software developers and engineers [85] to enable them to stay ahead of new technologies.…”

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confidence: 99%

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

Kokol

2024

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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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