  Code search engine is an essential tool in software development. Many code
search methods have sprung up, focusing on the overall ranking performance of
code search. In this paper, we study code search from another perspective by
analyzing the bias of code search models. Biased code search engines provide
poor user experience, even though they show promising overall performance. Due
to different development conventions (e.g., prefer long queries or
abbreviations), some programmers will find the engine useful, while others may
find it hard to get desirable search results. To mitigate biases, we develop a
general debiasing framework that employs reranking to calibrate search results.
It can be easily plugged into existing engines and handle new code search
biases discovered in the future. Experiments show that our framework can
effectively reduce biases. Meanwhile, the overall ranking performance of code
search gets improved after debiasing.
