Empirical auto-Tuning is getting attention in the field of high-performance computing (HPC) because it effectively reduces programmers' burden to improve the execution performance of an application. In the tuning process, a programmer selects a high-performance kernel variant of the application by evaluating the performances of multiple kernel variants. Since HPC applications need quite a huge number of floating-point operations, not all kernel variants produce exactly the same computation result as the original code. Although it is possible to verify the correctness of each kernel variant by executing the whole application to the end, it takes a long time to verify the final computation results of all kernel variants especially in the cases of long-running applications. Therefore, this paper proposes a framework that reduces the time for verifying the computation result on tuning a large-scale application. The framework uses user-specified information of the final computation result of the application to verify the correctness of every kernel variant. The framework also automatically skips unnecessary verifications to reduce the overall verification time. As a result, the framework streamlines empirical auto-Tuning.