Author(s)

Aditya Raghavan

Affiliation(s)

Department of Computer Science, Indian Institute of Technology Bombay, India.

Corresponding Author

Aditya Raghavan

ABSTRACT

The software industry is facing growing complexities in effectively managing software development lifecycles, from navigating project planning challenges to ensuring timely resolution of software defects. In recent years, the emergence of deep learning, a powerful subset of artificial intelligence, has begun to transform the landscape of software management. This comprehensive review article provides a thorough examination of the current and future applications of deep learning in optimizing software development practices, enhancing project management capabilities, and improving the overall efficiency and quality of software delivery.

By automating the analysis of complex data patterns, deep learning algorithms have demonstrated the ability to augment the decision-making capabilities of software managers, leading to more informed, data-driven, and proactive management of software projects. This review delves into the key areas where deep learning is making a significant impact, including software defect prediction, effort estimation, project risk assessment, and workflow optimization. It also explores the challenges and considerations that must be addressed to ensure the successful integration of deep learning into software management, such as data availability and quality, model interpretability, and ethical implications.

Furthermore, the paper outlines emerging trends and future directions, including the integration of deep learning with other cutting-edge technologies, the shift towards continuous and adaptive software management, and the role of deep learning in fostering more collaborative and cognitive software development environments. By harnessing the power of deep learning, software management professionals can unlock new frontiers of data-driven decision-making, predictive analytics, and intelligent automation, ultimately leading to improved project outcomes, enhanced productivity, and increased customer satisfaction.

This review serves as a comprehensive guide for software managers, researchers, and industry stakeholders seeking to explore the transformative potential of deep learning in revolutionizing software management practices.

KEYWORDS

Deep learning; Software management; Project management; Software development lifecycle; Defect prediction; Effort estimation; Risk assessment; Workflow optimization; Data-driven decision-making; Predictive analytics; Intelligent automation

CITE THIS PAPER

Aditya Raghavan. Deep learning in software management: a comprehensive review. Journal of Trends in Applied Science and Advanced Technologies. 2024, 1(1): 12-17. DOI: https://doi.org/10.61784/asat3004.

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