A Learning to Rank Framework for Developer Recommendation in Software Crowdsourcing

Recently, crowdsourcing has been widely used in many tasks that computers are not good at such as image recognition, entity resolution or some question answering tasks. A key feature of these tasks is that they are all simple tasks even decision making tasks. People can deal with these tasks with common sense knowledge. However, different from crowdsourcing in a general domain, software crowdsourcing is more complex. Only people with software developing skills can finish these tasks which could take a long time. Thus, an essential component of building a successful software crowdsourcing platform is effective developer recommendation, which aims to match a given task to the right crowdworkers. In order to solve this problem, in this paper, we propose a learning to rank framework. Specifically, we first propose a CRF-based model to extract criterias (i.e. skills and locations) from descriptions. Task characteristics learned from their descriptions and developer´ characteristic distributions extracted from their historical tasks are fed into our learning to rank algorithms for developer recommendation together with some other features such as topic-based features. We have evaluated our approach on a large dataset crawled from a real-world software crowdsourcing platform. The experimental results show that our approach is feasible and effective.