TY - GEN
T1 - Context-Aware Automated Sprint Plan Generation for Agile Software Development
AU - Kula, Elvan
AU - Van Deursen, Arie
AU - Gousios, Georgios
PY - 2024
Y1 - 2024
N2 - Sprint planning is essential for the successful execution of agile software projects. While various prioritization criteria influence the selection of user stories for sprint planning, their relative importance remains largely unexplored, especially across different project contexts. In this paper, we investigate how prioritization criteria vary across project settings and propose a model for generating sprint plans that are tailored to the context of individual teams. Through a survey conducted at ING, we identify urgency, sprint goal alignment, and business value as the top prioritization criteria, influenced by project factors such as resource availability and client type. These results highlight the need for contextual support in sprint planning. To address this need, we develop an optimization model that generates sprint plans aligned with the specific goals and performance of a team. By integrating teams' planning objectives and sprint history, the model adapts to unique team contexts, estimating prioritization criteria and identifying patterns in planning behavior. We apply our approach to real-world data from 4,841 sprints at ING, demonstrating significant improvements in team alignment and sprint plan effectiveness. Our model boosts team performance by generating plans that deliver more business value, align more closely with sprint goals, and better mitigate delay risks. Overall, our results show that the efficiency and outcomes of sprint planning practices can be significantly improved through the use of context-aware optimization methods.
AB - Sprint planning is essential for the successful execution of agile software projects. While various prioritization criteria influence the selection of user stories for sprint planning, their relative importance remains largely unexplored, especially across different project contexts. In this paper, we investigate how prioritization criteria vary across project settings and propose a model for generating sprint plans that are tailored to the context of individual teams. Through a survey conducted at ING, we identify urgency, sprint goal alignment, and business value as the top prioritization criteria, influenced by project factors such as resource availability and client type. These results highlight the need for contextual support in sprint planning. To address this need, we develop an optimization model that generates sprint plans aligned with the specific goals and performance of a team. By integrating teams' planning objectives and sprint history, the model adapts to unique team contexts, estimating prioritization criteria and identifying patterns in planning behavior. We apply our approach to real-world data from 4,841 sprints at ING, demonstrating significant improvements in team alignment and sprint plan effectiveness. Our model boosts team performance by generating plans that deliver more business value, align more closely with sprint goals, and better mitigate delay risks. Overall, our results show that the efficiency and outcomes of sprint planning practices can be significantly improved through the use of context-aware optimization methods.
KW - agile methods
KW - context-aware optimization
KW - sprint planning
UR - http://www.scopus.com/inward/record.url?scp=85212386249&partnerID=8YFLogxK
U2 - 10.1145/3691620.3695540
DO - 10.1145/3691620.3695540
M3 - Conference contribution
AN - SCOPUS:85212386249
T3 - Proceedings - 2024 39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024
SP - 1745
EP - 1756
BT - Proceedings - 2024 39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024
PB - Association for Computing Machinery (ACM)
T2 - 39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024
Y2 - 28 October 2024 through 1 November 2024
ER -