The use of search‐based optimization techniques to schedule and staff software projects: an approach and an empirical study

Abstract

SUMMARYAllocating resources to a software project and assigning tasks to teams constitute crucial activities that affect project cost and completion time. Finding a solution for such a problem is NP-hard; this requires managers to be supported by proper tools in performing such an allocation. This paper shows how search-based optimization techniques can be combined with a queuing simulation model to address these problems. The obtained staff and task allocations aim to minimize the completion time and reduce schedule fragmentation. The proposed approach allows project managers to run multiple simulations, compare results and consider trade-offs between increasing the staffing level and anticipating the project completion date and between reducing the fragmentation and accepting project delays. The paper presents results from the application of the proposed search-based project planning approach to data obtained from two large scale commercial software maintenance projects.

Stakeholders' social interaction in requirements engineering of open source software

“…Abdel-Hamid [1] developed a system dynamics model for staffing estimation, and applied the model in NASA's DE-A project to analyze the decision of allocating up to 8 people in one team. Antoniol and his colleagues exploited queuing simulation [5] along with multi-objective optimization [63], and found that 46 was the optimal staffing level for a large maintenance project.…”

“…In fact, search-based approaches [1,5,63] provide dynamic restaffing capabilities so that managers can better allocate project resources at different stages of the software life cycle.…”

“…For instance, given estimates of development effort in person-hours and software size in function points, an optimal group size can be obtained by regression analysis [152] or Bayesian inference [153]. To balance multiple and often competing objectives, search-based optimization techniques are recently used to assess staffing needs in the presence of uncertainty [75], schedule fragmentation [63], and communication overhead [64]. 1 These approaches assist project managers in determining development team size, matching developer skills, arranging organizational structures, and making other important resource allocation decisions.…”

An Evolutionary Hyper-heuristic for the Software Project Scheduling Problem

“…However, the SPSP is particularly challenging when the project is large. The space of possible allocations of employees to tasks is enormous, and providing an optimal allocation of employees to tasks becomes a very difficult task [14]. It is impractical to use exact methods to solve medium or large SPSP instances.…”

“…It is impractical to use exact methods to solve medium or large SPSP instances. Evolutionary algorithms have been employed to solve the SPSP [1], [5], [12], [14], [16], [17]. Other metaheuristics have also been used, such as ant colony optimization and its variants [4], [6], [15].…”