Snitch: Boosting Float & Double Precision For Clearer Tests

When working with floating-point numbers in software testing, precision is paramount. Snitch, a C++ testing framework, unfortunately, has a visibility issue. Specifically, the framework doesn't display float and double values with enough decimal places. This can lead to misleading and confusing error messages. These kinds of issues are common in software development, particularly when dealing with the intricacies of floating-point arithmetic. Let's delve into the problem and see how it can be addressed.

The Core Problem: Insufficient Decimal Digits

The root of the problem lies in how Snitch formats and displays float and double values within its error messages and test outputs. When comparing two floating-point numbers that are very close to each other, the limited precision used by Snitch can mask the subtle differences between them. As a result, tests may fail unexpectedly, and the error messages won't give enough information to pinpoint the source of the problem. This is a common challenge in numerical computing and testing, where tiny discrepancies can lead to significant variations in behavior. For instance, comparing the values 1.0 and 1.00000012 might result in Snitch reporting both values as 1.000000e+00, leading to a false negative. The problem is due to Snitch's internal use of a fixed number of decimal places when displaying these values in reports. This fixed number doesn't account for the nature of float and double values.

To better explain, let's consider the following error message: CHECK(a == b), got: 1.000000e+00 != 1.000000e+00. While the values should be different, the lack of sufficient precision in the output makes them indistinguishable. This can make it very hard to debug, especially when dealing with complex calculations or subtle differences in data.

Where the Issue Resides

The imprecision issue originates from the snitch_append.cpp file, specifically at line 22, where hardcoded values of 6 and 15 are used for precision when formatting float and double values, respectively. These values are too low to accurately represent the full range of float and double values. The solution involves increasing the number of decimal places used in the output to better represent these floating-point numbers. Specifically, we should use 8 and 16, which more accurately reflect the precision offered by float and double data types.

Enhancing Precision: A Practical Solution

The preferred fix is to replace the hardcoded precision values with a more adaptable method. It is suggested to use std::numeric_limits<T>::max_digits10 - 1. This approach dynamically determines the maximum number of digits that can be represented by a given floating-point type T, then adjusts the output accordingly. The -1 accounts for the digits after the decimal point, ensuring that the displayed output is as precise as possible. This dynamic approach ensures that the output precision is always appropriate for the specific data types being tested. This enhancement significantly improves the readability of test reports, making it easier to identify and resolve issues related to floating-point comparisons.

Practical Example: Illustrating the Problem

To illustrate the issue, let's examine a straightforward example using a simple CMake project and Snitch as a regular library. This will help demonstrate how the lack of precision can cause tests to fail and why enhanced precision is important. The test cases compare floating-point values that should be distinct but appear identical due to the low precision in the output.

Project Configuration (CMake)

First, we set up a CMake project that includes Snitch as a library:

1. Project Setup: Create an empty CMake project. Then, define the necessary FetchContent to download and integrate Snitch from its GitHub repository.
2. Linking Snitch: Make the downloaded Snitch library available and link it to your test executable. This ensures the test files can use Snitch's testing functionality.
3. Source Files: Specify your test file, such as main.cpp, to be included in the executable.
4. Configuration: Use Visual Studio 2022 (version 17.14, MSVC 19.44) for building the project, using default x64 debug and release configurations. This ensures the compilation environment matches the test environment.

Code Example

Here’s a simple C++ code snippet that demonstrates the issue:

#include <snitch/snitch.hpp>

TEST_CASE("float", "") {
    const float a = 1, b = 1.00000012;
    CAPTURE(sizeof(float), a, b);
    CONSTEXPR_CHECK(a == b);
}

TEST_CASE("double", "") {
    const double a = 1, b = 1.0000000000000002;
    CAPTURE(sizeof(double), a, b);
    CONSTEXPR_CHECK(a == b);
}

In this example, the CONSTEXPR_CHECK macro compares floating-point numbers (float and double). The CAPTURE macro is used to display the values and sizes during the test execution.

Expected Output and Problem Illustration

With insufficient precision, the test cases would fail, showing the same output values for a and b. The CAPTURE macro and CONSTEXPR_CHECK macro combined would result in an error message that fails to differentiate a and b due to the limited number of displayed decimal digits. As a result, the tests fail because the comparison returns false due to the internal limitations of Snitch's precision handling. Enhancing the precision in the output will resolve this issue, allowing for more reliable and accurate test results.

Impact and Importance of the Fix

The implications of this fix extend beyond simple clarity in test reports. Improved precision in Snitch's output helps to catch subtle errors that might otherwise go unnoticed. This is especially vital in scientific computing, financial modeling, and any field where the accuracy of floating-point calculations is critical. By increasing the precision, the debugging process becomes more effective, and test failures provide meaningful insights into the underlying problems.

This fix also aligns with best practices for testing and debugging, as it improves the overall quality of the testing framework. When test results are accurate and informative, developers can diagnose and fix issues more efficiently, leading to more robust and reliable software. The correction contributes to the reliability of tests and helps make them more effective in finding errors.

Conclusion: Improving Snitch's Precision

Fixing the precision issue in Snitch's output is critical for improving the reliability and usefulness of the testing framework. By increasing the number of decimal digits used to display floating-point values, test reports become more accurate, and debugging becomes more efficient. This change ensures that Snitch can accurately represent and compare floating-point numbers, thus helping developers detect and fix issues more effectively. The suggested method, using std::numeric_limits<T>::max_digits10 - 1, offers a robust and adaptable solution, improving the quality and usability of the testing framework. This enhancement helps create a more reliable testing environment and ultimately leads to better software. By addressing this precision problem, Snitch can continue to be a valuable tool for software testing and development.

For more insights into the principles of floating-point arithmetic and its impact on software development, consider the resources available on IEEE.