Journal of Information Processing

Automatic Optimize-time Validation for Binary Optimizers

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We propose an approach called automatic optimize-time validation for binary optimizers. Our approach does not involve executing the whole program for validation but selecting a small part of code (1 to 100 instructions) for the target test code. It executes the target code and its optimized code with several input data during binary opti-mization. One benefit is that we can test a small part of an actual customer’s code during binary optimization. Our approach can be used to test several input data not included in the target code, which is the most beneficial aspect of the approach. If the results are the same after execution, we will use the optimized code for the final output code. If the results differ, we can consider a couple of option, e.g., while developing a binary optimizer, we can abort the compilation with an error message to easily detect a bug. After a binary optimizer becomes generally available, we can use the input code for the final output code to maintain compatibility. Our goal is for the output binary code to be compatible, fast, and small. We focused on how to improve compatibility in this study. We implemented our approach in our binary optimizer and successfully detected one new bug. We used a very small binary program to observe the worst case of increased compilation time and output binary file size. Our implementation showed that our approach increases optimization time by only 0.02% and output binary file size by 8%.