Managing the development of large software systems: concepts and techniques
ICSE '87 Proceedings of the 9th international conference on Software Engineering
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Rapid application development
The 4+1 View Model of Architecture
IEEE Software
New directions on agile methods: a comparative analysis
Proceedings of the 25th International Conference on Software Engineering
User Stories Applied: For Agile Software Development
User Stories Applied: For Agile Software Development
Empirical studies of agile software development: A systematic review
Information and Software Technology
Agile Data Warehousing: Delivering World-Class Business Intelligence Systems Using Scrum and XP
Agile Data Warehousing: Delivering World-Class Business Intelligence Systems Using Scrum and XP
Modern software engineering methodologies meet data warehouse design: 4WD
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Multi-sprint planning and smooth replanning: An optimization model
Journal of Systems and Software
A Lagrangian heuristic for sprint planning in agile software development
Computers and Operations Research
| Hi-index | 0.00 |
Agile methods have been increasingly adopted to make data warehouse design faster and nimbler. They divide a data warehouse project into sprints (iterations), and include a sprint planning phase that is critical to ensure the project success. Several factors impact on the optimality of a sprint plan, e.g., the estimated complexity, business value, and affinity of the elemental functionalities included in each sprint, which makes the planning problem difficult. In this paper we formalize the planning problem and propose an optimization model that, given the estimates made by the project team and a set of development constraints, produces an optimal sprint plan that maximizes the business value perceived by users. The planning problem is converted into a multi-knapsack problem with constraints, given a linear programming formulation, and solved using the IBM ILOG CPLEX Optimizer. Finally, the proposed approach is validated through effectiveness and efficiency tests.