The Experts below are selected from a list of 69 Experts worldwide ranked by ideXlab platform
Elisa Turricchia - One of the best experts on this subject based on the ideXlab platform.
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a lagrangian heuristic for Sprint Planning in agile software development
Computers & Operations Research, 2014Co-Authors: Marco Antonio Boschetti, Stefano Rizzi, Matteo Golfarelli, Elisa TurricchiaAbstract:Agile methods for software development promote iterative design and implementation. Most of them divide a project into functionalities, called user stories; at each iteration, often called a Sprint, a subset of user stories are developed. The Sprint Planning phase is critical to ensure the project success, but it is also a difficult problem because several factors impact on the optimality of a Sprint plan, e.g., the estimated complexity, business value, and affinity of the user stories to be included in each Sprint. In this paper we present an approach for Sprint Planning based on an integer linear programming model. Given the estimates made by the project team and a set of development constraints, the optimal solution of the model is a Sprint plan that maximizes the business value perceived by users. Solving to optimality the model by a general-purpose MIP solver, such as IBM Ilog Cplex, takes time and for some instances even finding a feasible solution requires too large computing times for an operational use. For this reason we propose an effective Lagrangian heuristic based on a relaxation of the proposed model and some greedy and exchange algorithms. Computational results on both real and synthetic projects show the effectiveness of the proposed approach.
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multi Sprint Planning and smooth rePlanning
Journal of Systems and Software, 2013Co-Authors: Matteo Golfarelli, Stefano Rizzi, Elisa TurricchiaAbstract:HighlightsWe propose a model to produce multi-Sprint optimal plans for agile projects.Optimal plans maximize the business value perceived by users.Plans can be smoothly revised and re-optimized during project execution.Our model is validated on two case studies and on a set of synthetic projects. Most agile methods divide a 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, 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 a multi-Sprint optimal plan that maximizes the business value perceived by users. To cope with the inherent flexibility and uncertainty of agile projects, our approach ensures that a baseline plan can be revised and re-optimized during project execution without disrupting it, which we call smooth rePlanning. The Planning problem is converted into a generalized assignment problem, given a linear programming formulation, and solved using the IBM ILOG CPLEX Optimizer. Our model is validated on both real and synthetic projects. In particular, a case study on two real projects confirms the effectiveness of our approach; as to efficiency, for medium-sized problems an exact solution is found in a few minutes, while for large problems a heuristic solution that is less than 1% far from the exact one is returned in a few seconds. Finally, some smooth rePlanning tests investigate the trade-off between plan quality and stability.
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Sprint Planning optimization in agile data warehouse design
Data Warehousing and Knowledge Discovery, 2012Co-Authors: Matteo Golfarelli, Stefano Rizzi, Elisa TurricchiaAbstract: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.
Jacob Chiaan Tsai - One of the best experts on this subject based on the ideXlab platform.
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the role of Sprint Planning and feedback in game development projects implications for game quality
Journal of Systems and Software, 2019Co-Authors: Jingwei Liu, Jamie Y T Chang, Jacob Chiaan TsaiAbstract:Abstract Game development projects adopt Scrum to leverage their flexibility, as game concepts and the customer preferences are highly abstract and unpredictable. The most desirable features in an original game will not be easily identified during the first phase of development but will emerge later in a clear pattern as developers and testers continuously playtest the game. Thus, game development projects use feedback from game testers to understand what they think of various features and concepts, to obtain a better understanding of problem spaces. This study proposes that game tester feedback moderates the effect of Sprint Planning on game quality. A field study was conducted using a pair-matched questionnaire in which 102 game development projects participated. Results showed that Sprint Planning has a positive effect on game quality. The results also revealed that iterative feedback has a moderating effect on the relationship between Sprint Planning and game quality. Theoretical and practical implications are discussed.
Jingwei Liu - One of the best experts on this subject based on the ideXlab platform.
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the role of Sprint Planning and feedback in game development projects implications for game quality
Journal of Systems and Software, 2019Co-Authors: Jingwei Liu, Jamie Y T Chang, Jacob Chiaan TsaiAbstract:Abstract Game development projects adopt Scrum to leverage their flexibility, as game concepts and the customer preferences are highly abstract and unpredictable. The most desirable features in an original game will not be easily identified during the first phase of development but will emerge later in a clear pattern as developers and testers continuously playtest the game. Thus, game development projects use feedback from game testers to understand what they think of various features and concepts, to obtain a better understanding of problem spaces. This study proposes that game tester feedback moderates the effect of Sprint Planning on game quality. A field study was conducted using a pair-matched questionnaire in which 102 game development projects participated. Results showed that Sprint Planning has a positive effect on game quality. The results also revealed that iterative feedback has a moderating effect on the relationship between Sprint Planning and game quality. Theoretical and practical implications are discussed.
Matteo Golfarelli - One of the best experts on this subject based on the ideXlab platform.
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a lagrangian heuristic for Sprint Planning in agile software development
Computers & Operations Research, 2014Co-Authors: Marco Antonio Boschetti, Stefano Rizzi, Matteo Golfarelli, Elisa TurricchiaAbstract:Agile methods for software development promote iterative design and implementation. Most of them divide a project into functionalities, called user stories; at each iteration, often called a Sprint, a subset of user stories are developed. The Sprint Planning phase is critical to ensure the project success, but it is also a difficult problem because several factors impact on the optimality of a Sprint plan, e.g., the estimated complexity, business value, and affinity of the user stories to be included in each Sprint. In this paper we present an approach for Sprint Planning based on an integer linear programming model. Given the estimates made by the project team and a set of development constraints, the optimal solution of the model is a Sprint plan that maximizes the business value perceived by users. Solving to optimality the model by a general-purpose MIP solver, such as IBM Ilog Cplex, takes time and for some instances even finding a feasible solution requires too large computing times for an operational use. For this reason we propose an effective Lagrangian heuristic based on a relaxation of the proposed model and some greedy and exchange algorithms. Computational results on both real and synthetic projects show the effectiveness of the proposed approach.
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multi Sprint Planning and smooth rePlanning
Journal of Systems and Software, 2013Co-Authors: Matteo Golfarelli, Stefano Rizzi, Elisa TurricchiaAbstract:HighlightsWe propose a model to produce multi-Sprint optimal plans for agile projects.Optimal plans maximize the business value perceived by users.Plans can be smoothly revised and re-optimized during project execution.Our model is validated on two case studies and on a set of synthetic projects. Most agile methods divide a 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, 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 a multi-Sprint optimal plan that maximizes the business value perceived by users. To cope with the inherent flexibility and uncertainty of agile projects, our approach ensures that a baseline plan can be revised and re-optimized during project execution without disrupting it, which we call smooth rePlanning. The Planning problem is converted into a generalized assignment problem, given a linear programming formulation, and solved using the IBM ILOG CPLEX Optimizer. Our model is validated on both real and synthetic projects. In particular, a case study on two real projects confirms the effectiveness of our approach; as to efficiency, for medium-sized problems an exact solution is found in a few minutes, while for large problems a heuristic solution that is less than 1% far from the exact one is returned in a few seconds. Finally, some smooth rePlanning tests investigate the trade-off between plan quality and stability.
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Sprint Planning optimization in agile data warehouse design
Data Warehousing and Knowledge Discovery, 2012Co-Authors: Matteo Golfarelli, Stefano Rizzi, Elisa TurricchiaAbstract: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.
Stefano Rizzi - One of the best experts on this subject based on the ideXlab platform.
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a lagrangian heuristic for Sprint Planning in agile software development
Computers & Operations Research, 2014Co-Authors: Marco Antonio Boschetti, Stefano Rizzi, Matteo Golfarelli, Elisa TurricchiaAbstract:Agile methods for software development promote iterative design and implementation. Most of them divide a project into functionalities, called user stories; at each iteration, often called a Sprint, a subset of user stories are developed. The Sprint Planning phase is critical to ensure the project success, but it is also a difficult problem because several factors impact on the optimality of a Sprint plan, e.g., the estimated complexity, business value, and affinity of the user stories to be included in each Sprint. In this paper we present an approach for Sprint Planning based on an integer linear programming model. Given the estimates made by the project team and a set of development constraints, the optimal solution of the model is a Sprint plan that maximizes the business value perceived by users. Solving to optimality the model by a general-purpose MIP solver, such as IBM Ilog Cplex, takes time and for some instances even finding a feasible solution requires too large computing times for an operational use. For this reason we propose an effective Lagrangian heuristic based on a relaxation of the proposed model and some greedy and exchange algorithms. Computational results on both real and synthetic projects show the effectiveness of the proposed approach.
-
multi Sprint Planning and smooth rePlanning
Journal of Systems and Software, 2013Co-Authors: Matteo Golfarelli, Stefano Rizzi, Elisa TurricchiaAbstract:HighlightsWe propose a model to produce multi-Sprint optimal plans for agile projects.Optimal plans maximize the business value perceived by users.Plans can be smoothly revised and re-optimized during project execution.Our model is validated on two case studies and on a set of synthetic projects. Most agile methods divide a 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, 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 a multi-Sprint optimal plan that maximizes the business value perceived by users. To cope with the inherent flexibility and uncertainty of agile projects, our approach ensures that a baseline plan can be revised and re-optimized during project execution without disrupting it, which we call smooth rePlanning. The Planning problem is converted into a generalized assignment problem, given a linear programming formulation, and solved using the IBM ILOG CPLEX Optimizer. Our model is validated on both real and synthetic projects. In particular, a case study on two real projects confirms the effectiveness of our approach; as to efficiency, for medium-sized problems an exact solution is found in a few minutes, while for large problems a heuristic solution that is less than 1% far from the exact one is returned in a few seconds. Finally, some smooth rePlanning tests investigate the trade-off between plan quality and stability.
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Sprint Planning optimization in agile data warehouse design
Data Warehousing and Knowledge Discovery, 2012Co-Authors: Matteo Golfarelli, Stefano Rizzi, Elisa TurricchiaAbstract: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.
