Developer Recommendation for Crowdsourced Software Development Tasks

Crowdsourced software development utilises an open call format to attract geographically distributed developers to accomplish various types of software development tasks. Although the open call format enables wide task accessibility, potential developers must choose from a dauntingly large set of task options (usually more than one hundred available tasks on TopCoder each day). Inappropriate developer-task matching may lower the quality of the software deliverables. In this paper, we employ content-based recommendation techniques to automatically match tasks and developers. The approach learns particular interests from registration history and mines winner history to favour appropriate developers. We measure the performance of our approach by defining accuracy and diversity metrics. We evaluate our recommendation approach by introducing 4 machine learners on 3,094 historical tasks from TopCoder. The evaluation results show that promising accuracy and diversity are achievable (accuracy from 50% to 71% and diversity from 40% to 52% when recommending reliable developers). We also provide advice extracted from our results to guide the crowdsourcing platform in building a recommender system in practice.