AI-Driven Jira Automation: Using Machine Learning to Optimize Sprint Planning and Incident Resolution
Keywords:
AI-Driven Jira Automation, Sprint Planning Optimization, Incident Resolution, Machine Learning in Jira, IT Service Management (ITSM), DevOps Automation, Agile Project Management, Enterprise Jira Architect.Abstract
Large organizations achieve changes to their Agile sprint planning and IT service management (ITSM) incidents management by using Jira automation powered by AI technology. The achievement of growth prompts organizations to implement automation to improve their outdated manual Jira-based processes. Previous information analyzed by Jira ML algorithms enables forecasting workforce demands and improving sprint planning efficiency and self-executable incident detection and solution deployment. AI-enhanced Jira delivers improved operational effectiveness that reduces mistakes and accelerates organizational decision-making to become an indispensable tool in present-day enterprise projects. The system automatically detects IT incidents by utilizing AI capabilities and forecasting future occurrences that help adjust sprint planning for improved resource scheduling. Organizations achieve optimized workflow systems and production enhancement through artificial intelligence system implementation, resulting in better cross-departmental team collaboration. AI predictive analysis lets businesses identify upcoming operational threats, thus enabling them to develop effective protective strategies to maintain stable operations. The article investigates how machine learning helps Jira by enhancing its operational efficiency through multi-stage planning and incident management functions. As part of their duties, the Enterprise Jira Architect and automation Strategist develop adaptable linked Jira platforms that support company objectives within predefined standards. AI integration will fundamentally transform Jira-based business management systems because it is essential for upcoming success and business expansion.
References
Antunes, P., & Mourão, H. (2011). Resilient business process management: framework and services. Expert Systems with Applications, 38(2), 1241-1254.
Banerjee, S., Singh, P. K., & Bajpai, J. (2018). A comparative study on decision-making capability between human and artificial intelligence. In Nature Inspired Computing: Proceedings of CSI 2015 (pp. 203-210). Springer Singapore.
Blackburn, M., Busser, R., & Nauman, A. (2004, March). Why model-based test automation is different and what you should know to get started. In International conference on practical software quality and testing (pp. 212-232).
Carter, E., & Hurst, M. (2019). Agile Machine Learning. New York: Apress.
Chavan, A. (2021). Eventual consistency vs. strong consistency: Making the right choice in microservices. International Journal of Software and Applications, 14(3), 45-56. https://ijsra.net/content/eventual-consistency-vs-strong-consistency-making-right-choice-microservices
Chavan, A. (2022). Importance of identifying and establishing context boundaries while migrating from monolith to microservices. Journal of Engineering and Applied Sciences Technology, 4, E168. http://doi.org/10.47363/JEAST/2022(4)E168
Chen, Z., Kang, Y., Li, L., Zhang, X., Zhang, H., Xu, H., ... & Lyu, M. R. (2020, November). Towards intelligent incident management: why we need it and how we make it. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 1487-1497).
Fung, H. P. (2014). Criteria, use cases and effects of information technology process automation (ITPA). Advances in Robotics & Automation, 3.
Gann, D. M., & Salter, A. J. (2000). Innovation in project-based, service-enhanced firms: the construction of complex products and systems. Research policy, 29(7-8), 955-972.
Georgakopoulos, D., Hornick, M., & Sheth, A. (1995). An overview of workflow management: From process modeling to workflow automation infrastructure. Distributed and parallel Databases, 3, 119-153.
Hair Jr, J. F. (2007). Knowledge creation in marketing: the role of predictive analytics. European Business Review, 19(4), 303-315.
Hoang, N. M., & Shrestha, S. (2014). Project management software and its utilities: case: JIRA and Microsoft Project.
Imroz, S. M. (2016). A QUALITATIVE CASE STUDY IDENTIFYING METRICS FOR ITIL® REQUEST FULFILLMENT PROCESS TO CREATE EXECUTIVE DASHBOARDS: PERSPECTIVES OF AN INFORMATION TECHNOLOGY SERVICE PROVIDER GROUP.
Khoshgoftaar, T. M., & Seliya, N. (2003). Software quality classification modeling using the SPRINT decision tree algorithm. International Journal on Artificial Intelligence Tools, 12(03), 207-225.
Kingston, J. (2017). Using artificial intelligence to support compliance with the general data protection regulation. Artificial Intelligence and Law, 25(4), 429-443.
Kleissner, C. (1998, January). Data mining for the enterprise. In Proceedings of the Thirty-First Hawaii International Conference on System Sciences (Vol. 7, pp. 295-304). IEEE.
Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST, and SCA tools. International Journal of Science and Research Archive. https://ijsra.net/content/role-notification-scheduling-improving-patient
Koop, J. (2020). Automated Jira Data Analysis for Optimised Project Supervision and Delay Detection.
Kortum, F., Karras, O., Klünder, J., & Schneider, K. (2019). Towards a Better Understanding of Team-Driven Dynamics in Agile Software Projects: A Characterization and Visualization Support in JIRA. In Product-Focused Software Process Improvement: 20th International Conference, PROFES 2019, Barcelona, Spain, November 27–29, 2019, Proceedings 20 (pp. 725-740). Springer International Publishing.
Kumar, A. (2019). The convergence of predictive analytics in driving business intelligence and enhancing DevOps efficiency. International Journal of Computational Engineering and Management, 6(6), 118-142. Retrieved from https://ijcem.in/wp-content/uploads/THE-CONVERGENCE-OF-PREDICTIVE-ANALYTICS-IN-DRIVING-BUSINESS-INTELLIGENCE-AND-ENHANCING-DEVOPS-EFFICIENCY.pdf
Li, P. (2016). Jira 7 Essentials. Packt Publishing Ltd.
Malik, P. (2013). Governing big data: principles and practices. IBM Journal of Research and Development, 57(3/4), 1-1.
Malinowski, J., Weitzel, T., & Keim, T. (2008). Decision support for team staffing: An automated relational recommendation approach. Decision Support Systems, 45(3), 429-447.
Mosier, K. L., Fischer, U., Burian, B. K., & Kochan, J. A. (2017). Autonomous, context-sensitive, task management systems and decision support tools I: Human-autonomy teaming fundamentals and state of the art.
Mounce, S. R., Boxall, J. B., & Machell, J. (2010). Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows. Journal of Water Resources Planning and Management, 136(3), 309-318.
Nyati, S. (2018). Revolutionizing LTL carrier operations: A comprehensive analysis of an algorithm-driven pickup and delivery dispatching solution. International Journal of Science and Research (IJSR), 7(2), 1659-1666. Retrieved from https://www.ijsr.net/getabstract.php?paperid=SR24203183637
Penta, H. (2004). A COMPREHENSIVE TESTING APPROACH USING JEST FOR REACT NATIVE MOBILE APPLICATIONS (Doctoral dissertation, CALIFORNIA STATE UNIVERSITY, NORTHRIDGE).
Saarela, A. (2017). Deployment of the agile risk management with Jira into complex product development ecosystem (Bachelor's thesis, A. Saarela).
Salameh, H. (2014). What, when, why, and how? A comparison between agile project management and traditional project management methods. International Journal of Business and Management Review, 2(5), 52-74.
Sardana, J. (2022). Scalable systems for healthcare communication: A design perspective. International Journal of Science and Research Archive. https://doi.org/10.30574/ijsra.2022.7.2.0253
Sardana, J. (2022). The role of notification scheduling in improving patient outcomes. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notification-scheduling-improving-patient
Singh, V. (2022). Multimodal deep learning: Integrating text, vision, and sensor data: Developing models that can process and understand multiple data modalities simultaneously. International Journal of Research in Information Technology and Computing. https://romanpub.com/ijaetv4-1-2022.php
Singh, V., Doshi, V., Dave, M., Desai, A., Agrawal, S., Shah, J., & Kanani, P. (2020). Answering Questions in Natural Language About Images Using Deep Learning. In Futuristic Trends in Networks and Computing Technologies: Second International Conference, FTNCT 2019, Chandigarh, India, November 22–23, 2019, Revised Selected Papers 2 (pp. 358-370). Springer Singapore. https://link.springer.com/chapter/10.1007/978-981-15-4451-4_28
Stauffer, C., & Grimson, W. E. L. (1999, June). Adaptive background mixture models for real-time tracking. In Proceedings. 1999 IEEE computer society conference on computer vision and pattern recognition (Cat. No PR00149) (Vol. 2, pp. 246-252). IEEE.
Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud computing. Journal of network and computer applications, 34(1), 1-11.
Taddeo, G. (2020). A virtual assistant to manage Cloud performance monitoring tools.
Vegt, C. R. (2021). Analysing and visualising data to improve the productivity level of an Agile organised company (Bachelor's thesis, University of Twente).
Virtanen, J. (2021). Comparing Different CI/CD Pipelines.
Wamba-Taguimdje, S. L., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business process management journal, 26(7), 1893-1924.
Yu, L., & Guerra, C. (2019). Exploring the disruptive power of adopting DevOps for software development.
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