An Ontology-Based Model for Task Recommendation in Crowdsourced Software Engineering Environment
Abstract
Assigning tasks to appropriate people in crowd-based software engineering has always been an arduous task for a manager because the volume of the crowd to which a task is assigned is increasing day by day, and the process of selecting which task should be allocated to which person is becoming difficult. In order to make task recommendations more effective and less time-consuming, much research has been done. However, the problem is that such research does not contain the required implementation strategy and current recommendation techniques in crowdsourcing use workers’ history and mining methods, which cannot relate workers with tasks and other workers. This research aims to incorporate semantic capabilities in task allocation in a crowdsourced software engineering environment so that machines could guide crowdsourcing managers in choosing appropriate crowd workers for a task to be solved. In order to solve this problem and allow the machine to prescribe which resource is more suitable for a task semantically, an ontology-based recommendation technique is proposed. Ontologies relate information in a way that the machine can understand its semantics. These ontologies, along with data, create a knowledge base upon which semantic web works. Two ontologies, one of the crowds to which the task must be assigned and one of the tasks under consideration, have been developed to incorporate semantic capabilities in the crowdsourced task allocation process. These ontologies were designed keeping in view various factors obtained through analyzing different researches done to improve the task allocation in crowdsourcing. Then these ontologies were developed using RDF and OWL languages, and, in the end, these ontologies were tested using SPARQL. The performance and accuracy of the results returned by queries were also measured to know the efficiency of this approach in task allocation in a crowdsourcing environment. This approach can save time and provide more efficiency in a manager responsible for assigning tasks to different persons in a crowd-based software engineering environment.
Keywords: crowdsourcing, software engineering, semantic web, ontology, open call.
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