A classification and systematic review of product line feature model defects

Abstract

This article addresses the problem of assigning heterogeneous tasks with multiple skill requirements in crowdsourcing platforms. The aim is to find mutually exclusive, a highly-productive set of workers who can successfully complete the tasks within a given deadline and budget. We propose a timeline based weighted aggregation (TWA) technique to quantify the per-skill score of a worker. The score is computed based on the worker’s profile and past work experiences. Given the worker’s score, we formulate the problem as one of maximizing the productivity of all the NN given tasks. A two-stage approximation solution is proposed. In the first stage, we offer a greedy-based 2-approximation algorithm for a single task. In the second stage, a local ratio based algorithm is proposed to extend the solution for multiple tasks. The overall solution is shown to be 3-approximate. Finally, simulation results using real-world data are presented to highlight the efficacy of our proposed schemes.