AntMiner: Mining More Bugs by Reducing Noise Interference

Detecting bugs with code mining has proven to be an effective approach. However, the existing methods suffer from reporting serious false positives and false negatives. In this paper, we developed an approach called AntMiner to improve the precision of code mining by carefully preprocessing the source code. Specifically, we employ the program slicing technique to decompose the original source repository into independent sub-repositories, taking critical operations (automatically extracted from source code) as slicing criteria. In this way, the statements irrelevant to a critical operation are excluded from the corresponding sub-repository. Besides, various semantics-equivalent representations are normalized into a canonical form. Eventually, the mining process can be performed on a refined code database, and false positives and false negatives can be significantly pruned. We have implemented AntMiner and applied it to detect bugs in the Linux kernel. It reported 52 violations that have been either confirmed as real bugs by the kernel development community or fixed in new kernel versions. Among them, 41 cannot be detected by a widely used representative analysis tool Coverity. Besides, the result of a comparative analysis shows that our approach can effectively improve the precision of code mining and detect subtle bugs that have previously been missed.