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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 many finite decimal numbers, such as 0.1, because these numbers have repeating binary representations.

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This noncompliant code example performs some basic currency calculations.:

double dollar = 1.00;
double dime = 0.10;
int number = 7;
System.out.println(
  "A dollar less " + number + " dimes is $" + (dollar - number * dime) 
);

Because the value 0.10 lacks an exact representation in either Java floating-point type (or any floating-point format that uses a binary mantissa), on most platforms, this program prints the following:

A dollar less 7 dimes is $0.29999999999999993

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This compliant solution uses an integer type (such as int) and works with cents rather than dollars.:

int dollar = 100;
int dime = 10;
int number = 7;
System.out.println(
  "A dollar less " + number + " dimes is $0." + (dollar - number * dime)
);

This code correctly outputs the following:

A dollar less 7 dimes is $0.30

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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 outputs the following:

A dollar less 7 dimes is $0.3

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Using floating-point representations when precise computation is required can result in a loss of precision and incorrect values.

Automated Detection

Automated detection of floating-point arithmetic is straight forwardstraightforward. 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.

Related Guidelines

Android Implementation Details

The use of floating-point on Android is not recommended for performance reasons on Android.

Bibliography

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