计算机科学 ›› 2020, Vol. 47 ›› Issue (3): 54-60.doi: 10.11896/jsjkx.191100085
所属专题： 智能软件工程
唐文君,张佳丽,陈荣,郭世凯
TANG Wen-jun,ZHANG Jia-li,CHEN Rong,GUO Shi-kai
摘要： 如何将众包测试任务分派给合适的众测工人,以较低的成本获得更好的测试结果,是一个重要问题。文中将CWS众测任务分派问题建模为一个基于马尔可夫决策过程的问题,且使用Deep Q Network进行学习和实时在线测试任务分派。该基于强化学习的方法被命名为WTA-C。此外,文中根据众测工人执行任务的历史时间,通过统计条件概率计算测试工人在任务期限内完成任务的概率,将其作为工人信誉值来反映工人质量,并在每次分派完成后对工人信誉值进行更新。实验结果显示,WTA-C在控制测试任务的“质量-成本”权衡和保证工人可靠度方面优于其他基于启发式策略的实时分派方法,并在分派效果上高于各启发式策略18%以上,从而证明了其可以更好地适应CWS的结构和众测环境的特点。
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