Inferring Automatic Test Oracles

William B. Langdon, Shin Yoo, Mark Harman
2017 2017 IEEE/ACM 10th International Workshop on Search-Based Software Testing (SBST)  
We propose the use of search based learning from existing open source test suites to automatically generate partially correct test oracles. We argue that mutation testing and nversion computing (augmented by deep learning and other soft computing techniques), will be able to predict whether a program's output is correct sufficiently accurately to be useful.
doi:10.1109/sbst.2017.1 dblp:conf/icse/LangdonYH17 fatcat:qky4eabxcjgwbg534il6x6rmqq