An Adaptive Recommender System for Human Resource Allocation in Software Projects - Initial Results on an Agent-Based Simulation

摘要

In our previous work we have introduced a skill-based mathematical model of resource allocation. This paper extends our skill based approach by introducing adaptive skill sets for employees and a history-based initial evaluation strategy. For this purpose, the mathematical model is adjusted in order to modify skill vectors after a task allocation. In turn this enables estimations of the time to task completion based on employee history. This approach would provide the team leaders with a better view of the skill sets mastered by the team. We experimentally evaluate the impact of the skill adjustment on the project duration and cost in a simulation environment. The conclusion of the experiment is that taking into account the implicit skill gain of employees during their daily activity decreases projected costs and execution time significantly, which is this paper's contribution to the state of the art. This approach is a good way to keep the team's skill sets automatically updated. The experiment is designed as an agent society simulation and through their interactions raw data is collected in order to calculate the performance measures. A scalability experiment is also presented showing slight (1%) decreases in project duration when the task number doubles while costs decrease between 7-32%.