Integrating AI into Kanban for Agile Mobile Product Development: Enhancing Workflow Efficiency, Real-Time Monitoring, and Task Prioritization
Downloads
Keywords:
Artificial Intelligence, Kanban, Agile methodologies, mobile product developmentAbstract
The integration of Artificial Intelligence (AI) into Kanban systems has emerged as a transformative approach to enhancing workflow efficiency, real-time monitoring, and task prioritization within Agile mobile product development. This paper aims to systematically investigate the intersection of AI and Kanban methodologies, elucidating how these technologies can synergistically improve the performance and adaptability of Agile teams in dynamic mobile development environments. With the increasing complexity of mobile applications and the rapid pace of technological advancements, traditional Kanban practices may fall short in addressing the nuanced challenges that contemporary development teams face. Hence, this research proposes a novel framework that leverages AI capabilities to augment Kanban practices, thus facilitating more intelligent decision-making processes.
Downloads
References
K. Schwaber and J. Sutherland, "The Scrum Guide," Scrum.org, 2020. [Online]. Available: https://www.scrumguides.org/scrum-guide.html.
M. K. Verma, "Kanban for Software Development: A Comprehensive Guide," International Journal of Software Engineering and Applications, vol. 8, no. 3, pp. 1-12, 2017.
M. R. Poppendieck and T. Poppendieck, Lean Software Development: An Agile Toolkit. Addison-Wesley, 2003.
Mahesh, Madhu. "Broker Incentives and Their Influence on Medicare Plan Selection: A Comparative Analysis of Medicare Advantage and Part D." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 493-512.
J. Singh, “Understanding Retrieval-Augmented Generation (RAG) Models in AI: A Deep Dive into the Fusion of Neural Networks and External Databases for Enhanced AI Performance”, J. of Art. Int. Research, vol. 2, no. 2, pp. 258–275, Jul. 2022
Tamanampudi, Venkata Mohit. "Natural Language Processing for Anomaly Detection in DevOps Logs: Enhancing System Reliability and Incident Response." African Journal of Artificial Intelligence and Sustainable Development 2.1 (2022): 97-142.
Bonam, Venkata Sri Manoj, et al. "Secure Multi-Party Computation for Privacy-Preserving Data Analytics in Cybersecurity." Cybersecurity and Network Defense Research 1.1 (2021): 20-38.
Thota, Shashi, et al. "Few-Shot Learning in Computer Vision: Practical Applications and Techniques." Human-Computer Interaction Perspectives 3.1 (2023): 29-59.
Vaithiyalingam, Gnanavelan. "Bridging the Gap: AI, Automation, and the Future of Seamless Healthcare Claims Processing." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 248-267.
Khan, Samira, and Hassan Khan. "Harnessing Automation and AI to Overcome Challenges in Healthcare Claims Processing: A New Era of Efficiency and Security." Distributed Learning and Broad Applications in Scientific Research 8 (2022): 154-174.
Singh, Jaswinder. "The Ethics of Data Ownership in Autonomous Driving: Navigating Legal, Privacy, and Decision-Making Challenges in a Fully Automated Transport System." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 324-366.
Tamanampudi, Venkata Mohit. "AI-Powered Continuous Deployment: Leveraging Machine Learning for Predictive Monitoring and Anomaly Detection in DevOps Environments." Hong Kong Journal of AI and Medicine 2.1 (2022): 37-77.
Ahmad, Tanzeem, et al. "Sustainable Project Management: Integrating Environmental Considerations into IT Projects." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 191-217.
J. M. Leach, "The Effect of Kanban on Software Development Performance: A Case Study," Journal of Software: Evolution and Process, vol. 28, no. 10, pp. 1-13, 2016.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
Plaudit
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.