Binary Folding Compression for Efficient Software Distribution

Jinheng Li, Qiao Li, Hu Wan, Chun Jason Xue

Abstract: This paper presents a simple yet effective approach for compressing binary files by detecting and folding similar patterns. Until now, methods for compressing these files were mostly limited to compiler optimization and traditional compression tools. However, our research suggests that there is an additional opportunity for compressing binary files and proposes an innovative way to allow multiple functions to share an extracted pattern code. The new method employs straightforward pattern recognition algorithms to spot recurring patterns in binary files and compresses them in order to reduce the file size. This results in a more efficient use of storage space and quicker data transfer speeds. Also, we introduce a novel feature that enables an extracted dynamic library to be shared among several binary files, further improving the method's effectiveness. We provide a detailed analysis comparing our method with the existing ones, showing that it performs better in a variety of practical implementations. This suggests that our humble attempt to improve binary file compression might pave the way for future developments in this field.