Does Bug Prediction Support Human Developers? Findings from a Google Case Study

Chris Lewis, Zhongpeng Lin, Caitlin Sadowski, Xiaoyan Zhu, Rong Ou, and E. James Whitehead Jr.

UC Santa Cruz, USA; Google, USA; Xi'an Jiaotong University, China

Track: Technical Research
Session: Bug Prediction
While many bug prediction algorithms have been developed by academia, they're often only tested and verified in the lab using automated means. We do not have a strong idea about whether such algorithms are useful to guide human developers. We deployed a bug prediction algorithm across Google, and found no identifiable change in developer behavior. Using our experience, we provide several characteristics that bug prediction algorithms need to meet in order to be accepted by human developers and truly change how developers evaluate their code.