Reputation Aware Fair Worker Selection in Collaborative Software Crowdsourcing

Soaring demand of software products without degrading the performance and meeting the expectation of maximum cost-profit satisfaction have made collaborative software crowdsourcing an immense essence of technological employment. The geographical, technological, and psychological variations among the crowdsourcing workers can delude the team selection procedure at a high rate, which ends up being a most challenging problem to select the best worker and form an efficient group. However, existing works in the literature suffer from the limitation of not having an efficient mechanism to select workers in a collaborative software crowdsourcing platform by maintaining a fair worker selection procedure. In this paper, we have developed an algorithm where the buyer has the facilities to select workers by prioritizing the worker's reputations. The performance analysis results, carried out in MATLAB, show that compared to other state-of-the-art works, the proposed Crowdsourcing Online Group (COG) system can achieve significant performance improvement in terms of user satisfaction by selecting workers with a high reputation and the system has stronger practicability.