Note | ||
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This rule may be deprecated and replaced by a similar guideline. 06/28/2014 -- Version 1.0 |
The Java language provides two primitive floating-point types, float
and double
, which are associated with the single-precision 32-bit and double-precision 64-bit format values and operations specified by IEEE 754 [IEEE 754]. Each of the floating-point types has a fixed, limited number of mantissa bits. Consequently, it is impossible to precisely represent any irrational number (for example, pi). Further, because these types use a binary mantissa, they cannot precisely represent Computers can represent only a finite number of digits. It is therefore impossible to precisely represent repeating binary-representation values. This includes many finite decimal numbers, such as 1/100.1, because these numbers have repeating binary representations.
When precise computation is necessary, and especially such as when doing performing currency calculations, consider alternative representations that may be able to completely represent values rather than the floating point representations float
and double
, floating-point types must not be used. Instead, use an alternative representation that can completely represent the necessary values.
When precise computation is unnecessary, floating-point representations may be used. In these cases, you must carefully and methodically estimate the maximum cumulative error of the computations to ensure that the resulting error is within acceptable tolerances. Consider using numerical analysis to properly understand the problem. See Goldberg's work for an introduction to this topic [Goldberg 1991].
Noncompliant Code Example
This very simple example attempts to do noncompliant code example performs some basic currency calculations:
Code Block | ||
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double dollar = 1.000; double dime = 0.110; int number = 7; System.out.println( ( "A dollar less " + number + " dimes is $" + (dollar - number * dime) ); |
Unfortunately, because of the imprecision of floating point arithmeticBecause the value 0.10 lacks an exact representation in Java floating-point type (or any floating-point format that uses a binary mantissa), on most platforms, this program prints the following:
Code Block |
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A dollar less 7 dimes is $0.29999999999999993 |
Compliant Solution
A better approach is to use This compliant solution uses an integer type (such as long
int
) and work in works with cents rather than dollars.:
Code Block | ||
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longint dollar = 100; longint dime = 10; int number = 7; System.out.println( ("A dollar less " + number + " dimes is $0." + (dollar - number * dime) + " cents" ); |
This code prints out:correctly outputs the following:
Code Block |
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A dollar less 7 dimes is $0.30 |
...
|
Compliant Solution
...
This compliant solution uses An alternative approach is to use the BigDecimal
type, which provides exact representation of decimal values. Note that on most platforms, computations performed using BigDecimal
are less efficient than those performed using primitive types.
Code Block | ||
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import java.math.BigDecimal; // ... BigDecimal dollar = new BigDecimal("1.0"); BigDecimal dime = new BigDecimal("0.1"); int number = 7; System.out.println ("A dollar less " + number + " dimes is $" + (dollar.subtract( new BigDecimal(number).multiply(dime) )) ); |
This code prints out:outputs the following:
Code Block |
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A dollar less 7 dimes is $0.3 |
Risk Assessment
Using a representation other than floating point may allow for more precision and accuracy for critical arithmetic.-point representations when precise computation is required can result in a loss of precision and incorrect values.
Rule |
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Severity | Likelihood | Remediation Cost | Priority | Level |
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NUM04-J |
Low |
Probable |
High | P2 | L3 |
Automated Detection
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TODO
Related Vulnerabilities
Search for vulnerabilities resulting from the violation of this rule on the CERT website.
Other Languages
Automated detection of floating-point arithmetic is straightforward. However, determining which code suffers from insufficient precision is not feasible in the general case. Heuristic checks, such as flagging floating-point literals that cannot be represented precisely, could be useful.
Tool | Version | Checker | Description | ||||||
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Parasoft Jtest |
| CERT.NUM04.UBD | Do not use "float" and "double" if exact answers are required |
Related Guidelines
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Avoid using floating point numbers when precise computation is needed | |
Floating-Point Arithmetic [PLF] |
Android Implementation Details
The use of floating-point on Android is not recommended for performance reasons.
Bibliography
Item 48, "Avoid | |
Puzzle 2, "Time for a Change" | |
[IEEE 754] | |
[JLS 2015] | |
[Seacord 2015] |
...
References
[JLS 05] Section 4.2.3 Floating-Point Types, Formats, and Values
[Bloch 08] Item 48: Avoid float
and double
if exact answers are required
[Bloch 05] Puzzle 2: Time for a Change