Investigating the Accuracy of Test Code Size Prediction using Use Case Metrics and Machine Learning Algorithms: An Empirical Study

@inproceedings{Badri2017InvestigatingTA,
  title={Investigating the Accuracy of Test Code Size Prediction using Use Case Metrics and Machine Learning Algorithms: An Empirical Study},
  author={Mourad Badri and Linda Badri and William Flageol and Fadel Tour{\'e}},
  booktitle={ICMLSC '17},
  year={2017}
}
Software testing plays a crucial role in software quality assurance. It is, however, a time and resource consuming process. It is, therefore, important to predict as soon as possible the effort required to test software, so that activities can be planned and resources can be optimally allocated. Test code size, in terms of Test Lines Of Code (TLOC), is an important testing effort indicator used in many empirical studies. In this paper, we investigate empirically the early prediction of TLOC for… CONTINUE READING