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Partial correctness means that, if the algorithm terminates on a permitted input, its output meets the formal specification. Total correctness additionally requires algorithm termination on every permitted input.

A precondition $P$ describes permitted starting circumstances; a postcondition $Q$ describes the required result. Partial correctness says that starting with $P$ and finishing implies $Q$. Total correctness also guarantees finishing. A loop invariant supplies the connection between these conditions during repeated steps.^[ref]

Correctness is relative to a formal specification. Returning the first entry of an array correctly implements "return the first entry", but fails "return the maximum" on $[3,1,4,4,2]$. A fast wrong answer does not acquire algorithm correctness from its time complexity. Numerical closeness matters only when the formal specification allows an approximation. Software testing and test coverage provide evidence about selected executions; they do not by themselves establish algorithm correctness for every permitted input.

Establishing specified properties of a system using formal proofs about a precise model. Formal verification connects an implementation to a formal specification; correctness of the model and its assumptions still matters when applying the result to a physical system.

For a small example, the summation loop verification below starts with executable steps and asks two separate questions: does any returned value satisfy the postcondition, and does execution reach that return? A loop invariant answers the first; a loop variant answers the second. This turns an unbounded family of permitted inputs into a few reusable proof obligations.

The model must specify programming language semantics, including the meaning of variable assignments, comparisons and integer arithmetic. A proof about exact mathematical integers need not describe a machine that wraps on integer overflow. Similarly, a correct formal proof of a weak or mistaken formal specification does not establish the intended behavior. Choosing the right formal specification and relating the model to the implemented system remain substantive tasks.

Software testing examines chosen executions and can expose a counterexample. A formal proof instead justifies a statement throughout its stated model; it does not require executing every permitted input. This makes formal verification valuable when rare boundary cases matter, while software testing can still reveal an incorrect model or misunderstood requirement.

In 1969, Tony Hoare presented rules for variable assignment, composition and iteration in a small programming language. His paper explicitly says those rules do not prove successful termination, and discusses the dependence of arithmetic reasoning on the chosen overflow behavior. These reservations already make the model's scope part of the argument.^[ref]