Borderline Case
/ˈbȯr-dər-ˌlīn kās/
noun — "the input that lives right on the edge of what a system expects."
A Borderline Case is a situation, input, condition, or value that falls at or near the limits of a system's expected behavior. It exists on the boundary between normal operation and exceptional behavior, often exposing assumptions, hidden bugs, or design weaknesses that may not appear during ordinary use.
Range
/reɪndʒ/
noun — “the span between what a system can hold and what it quietly refuses to remember.”
Range in computing is the set of values that can be represented within a given numeric system, data type, or storage format. It defines the lower and upper bounds of representable numbers before phenomena like overflow or underflow occur.
Underflow
/ˈʌn-dər-ˌfloʊ/
noun — “when numbers shrink so far they slip between the cracks of representation.”
Underflow is a condition in computing where a numeric value becomes too small to be represented accurately within a given format. It most commonly appears in floating-point systems, particularly those defined by IEEE 754, where numbers are stored using a finite number of bits for both magnitude and precision.
Overflow
/ˈoʊ-vər-ˌfloʊ/
noun — “what happens when a number tries to be bigger than the container pretending to hold it.”
Overflow is a condition in computing where a value exceeds the maximum representable range of a fixed-size numeric type. It occurs when an operation produces a result that cannot be stored within the allocated number of bits, especially in integer arithmetic, though it can also appear in floating-point systems in a different form.
Integer
/ˈɪn-tɪ-dʒər/
noun — “a number that refuses decimals, refuses ambiguity, and generally prefers to stay whole.”
Integer is a numeric type used in computing to represent whole numbers without fractional parts. Unlike floating-point values, integers are exact within their range: no rounding, no approximation, no hidden binary drift from rounding error. If a value is an integer, it is either fully representable or it simply does not fit.
Precision
/ˈprɛ-ʒɪ-ʒən/
noun — “how finely a system can distinguish truth from its nearest approximation.”
Precision is the measure of how detailed or exact a numerical representation or computation can be. In computing, it describes the degree to which a value can be represented without losing information due to rounding, truncation, or storage limits. It is closely tied to floating-point systems such as IEEE 754.
IEEE 754
/ˈaɪ-tri-ˈtri-fɪv-foʊr/
noun — “the rulebook that tells computers how to lie about real numbers… consistently.”
IEEE 754 is a widely adopted standard for representing and performing arithmetic on floating-point numbers in digital systems. It defines how real numbers are encoded into binary, how operations like addition and multiplication behave, and what happens when numbers become too large, too small, or mathematically undefined.
Rounding Error
/ˈraʊn-dɪŋ ˈer-ər/
noun — “the tiny mismatch between what a number is, and what a computer can afford to remember.”
Rounding error is the difference between an exact mathematical value and its finite approximation when represented in a digital system. It appears whenever a number must be shortened, simplified, or forced into a format that cannot store infinite precision, especially in floating-point arithmetic.
Float
/floʊt/
noun — “a number that refuses to sit still, always sliding between precision and approximation depending on context.”
Float (short for floating-point number) is a numerical representation used in computing to approximate real numbers using a finite amount of memory. Unlike integer types, which represent whole numbers exactly, a float is designed to handle fractions, extremely large values, and extremely small values by storing numbers in a scientific-notation-like format.
Decimal
/ˈdes-ə-məl/
noun — “base ten… the number system humans picked mostly because we have ten fingers and nobody bothered to renegotiate.”