A multi-objective agile project planning model and a comparative meta-heuristic approach

Abstract

Agile software development methodologies are used to meet the changing needs in the market. The most popular framework among these methodologies is the Scrum framework. In Scrum planning, the assignment of user stories to sprints requires the consideration of multiple objectives to use the limited resources more effectively. In this paper, a multi-objective mixed-integer programming model is developed which considers three objectives: maximizing the sprint capacity usage, maximizing the assignment of user stories with high priority to primary sprints, and maximizing the assignment of affine user stories to the same sprint. The aim is to contribute to both theory and practice of Scrum planning considering multiple objectives. Additionally, different from the existing literature of Scrum planning, alternative user stories are also taken into account. The proposed model is applied to the small, medium, and big-sized instances of the problem taken from a real-life system. Non-dominated Sorting Genetic Algorithm (NSGA-II) and Strong Pareto Evolutionary Algorithm (SPEA2) are used as heuristic approaches since big-sized instances of the problem could not be solved using optimization approaches. To analyze the performances of these algorithms, Hypervolume (HV), Epsilon (ϵ), Generational Distance (GD), Inverted Generational Distance (IGD), Inverted Generational Distance Plus (IGD+), and Spread (Δ) indicators are used. Results showed that NSGA-II performs better than SPEA2 according to ϵ indicator for big-sized instance. On the other hand, SPEA2 performs better than NSGA-II according to HV, GD, IGD, IGD+, and Δ indicators. However, the results are very close to each other for HV, ϵ, IGD, and IGD+ indicators. In conclusion, both algorithms can be used to deal with the multi-objective Scrum planning problem.