Co-changing code volume prediction through association rule mining and linear regression model

作者：

Highlights：

• We propose an approach to predicting co-changing code volume.

• The success rate of co-changing methods identification is 82%.

• Co-changing code volume is predicted using a derived regression line.

• MAE of predictions is 95.3% less than the one of a naive approach.

摘要

•We propose an approach to predicting co-changing code volume.•The success rate of co-changing methods identification is 82%.•Co-changing code volume is predicted using a derived regression line.•MAE of predictions is 95.3% less than the one of a naive approach.

论文关键词：Co-changing code volume prediction,Co-changing methods identification

论文评审过程：Received 8 May 2015, Revised 16 September 2015, Accepted 17 September 2015, Available online 30 September 2015, Version of Record 19 October 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.09.023