The Dynamics of Software Project Staffing: A System Dynamics Based Simulation Approach
IEEE Transactions on Software Engineering
A 'peak shaving' approach to project staff reallocation
Proceedings of the 23rd international conference on on Computers and industrial engineering
Software developer perceptions about software project failure: a case study
Journal of Systems and Software - Special issue on software engineering education and training for the next millennium
Metrics and Models in Software Quality Engineering
Metrics and Models in Software Quality Engineering
Assessing Staffing Needs for a Software Maintenance Project through Queuing Simulation
IEEE Transactions on Software Engineering
Assigning people to roles in software projects
Software—Practice & Experience
Emphasizing Human Capabilities in Software Development
IEEE Software
A Task Allocation Optimizer for Software Construction
IEEE Software
Formal model for assigning human resources to teams in software projects
Information and Software Technology
A fuzzy expert system architecture for capability assessments in skill-based environments
Expert Systems with Applications: An International Journal
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The completion of reliable software products within their expected time frame represents a major problem for companies that develop software applications. Today, the software industry continues to struggle with delivering products in a timely manner. A major cause for delays is the training time required for engineers and other personnel to acquire the necessary skills to complete software tasks. Therefore, it is important to develop systematic personnel assignment processes that consider complete skill sets of candidates to provide solutions that reduce training time. This paper presents a novel methodology to assign resources to tasks when optimum skill sets are not available. The methodology takes into account existing capabilities of candidates, required levels of expertise, and priorities of required skills for the task. A sample case is used to show the model capabilities, and the results are compared with the current resource assignment approach.