A dynamic program analysis-based method for floating-point program precision loss detection

Yaguang Zhang; Lei Wang; Yizhuo Wang; Fangjie Xu

doi:10.1117/12.2683198

26 June 2023 A dynamic program analysis-based method for floating-point program precision loss detection

Yaguang Zhang, Lei Wang, Yizhuo Wang, Fangjie Xu

Proceedings Volume 12714, International Conference on Computer Network Security and Software Engineering (CNSSE 2023); 127140H (2023) https://doi.org/10.1117/12.2683198
Event: Third International Conference on Computer Network Security and Software Engineering (CNSSE 2023), 2023, Sanya, China

Abstract

The loss of precision in floating-point programs is necessary to detect the loss of precision in floating-point programs because of the computational errors in floating-point operations, which can lead to the loss of precision in floating-point programs and affect the reliability of the upper-level software systems that call them. The inherent storage form of floating-point data makes it impossible to represent all real numbers fully, and the rounding error and error accumulation in floating-point operations make it difficult to detect the precision loss in floating-point programs. In this paper, we propose a dynamic analysis method to detect the precision loss of floating-point programs. First, we analyze the organization of the source code with the front-end Clang tool of LLVM to locate the floating-point data declarations and floating-point operations. Then we run the floating-point operations simultaneously with the higher precision MPFR operations with the help of the Dyninst staking framework to detect the precision loss at the statement level and record the precision loss of the floating-point. The precision loss of each step of the program is recorded. The precision loss change graph is generated to locate the floating-point precision loss. By testing the commonly used functions in the GSL library and some test cases in FPPench, the relative error of the method in this paper is improved by 43% on average compared with the randomly generated input data method to detect GSL functions. The relative error is improved by 1.8 bits on average compared with the Herbgrind tool for testing against FPPench.

Citation Download Citation

Yaguang Zhang, Lei Wang, Yizhuo Wang, and Fangjie Xu "A dynamic program analysis-based method for floating-point program precision loss detection", Proc. SPIE 12714, International Conference on Computer Network Security and Software Engineering (CNSSE 2023), 127140H (26 June 2023); https://doi.org/10.1117/12.2683198

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KEYWORDS

Error analysis

Computing systems

Statistical analysis

Computer programming languages

Detection and tracking algorithms

Mathematical optimization

Missiles

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