Abstract
Greedy and optimization approaches used for assigning trainees to software project requirements can give rise to unstable pairs. The presence of unstable pairs can unnecessarily hamper both trainee and project requirement satisfaction. In this paper, we use the theory of stable matching to remove the unstable pairs. We propose an efficient method to predict preferences for both trainees and project requirements using the utility theory. Our computational experiments suggest that the stable matching approach performs better with average trainee and project requirement satisfaction in terms of the preference ranks of allocated choices. Compared to cost based optimization model, the proposed approach obtains significantly better trainee project match at the expense of small additional allocation cost.
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