Using semi-supervised learning for predicting metamorphic relations
Using semi-supervised learning for predicting metamorphic relations
复制标题
使用半监督学习来预测变质关系
DOI：
10.1145/3193977.3193985
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Kanewala, Upulee
中科院分区：
文献类型：
--
作者：
Hardin, Bonnie;Kanewala, Upulee
Software testing is difficult to automate, especially in programs which have no oracle, or method of determining which output is correct. Metamorphic testing is a solution this problem. Metamorphic testing uses metamorphic relations to define test cases and expected outputs. A large amount of time is needed for a domain expert to determine which metamorphic relations can be used to test a given program. Metamorphic relation prediction removes this need for such an expert. We propose a method using semi-supervised machine learning to detect which metamorphic relations are applicable to a given code base. We compare this semi-supervised model with a supervised model, and show that the addition of unlabeled data improves the classification accuracy of the MR prediction model.
