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Semantics (programming languages)

Semantics (programming languages) is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Semantics (programming languages) rather than just read about it. In short: In programming language theory, semantics is the rigorous mathematical logic study of the meaning of programming languages. Semantics assigns computational meaning to valid strings in a programming language syntax.

Key takeaways

  • Semantics (programming languages) belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Semantics (programming languages) to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Semantics (programming languages) from memory before moving on to harder problems.

Reference excerpt

In programming language theory, semantics is the rigorous mathematical logic study of the meaning of programming languages. Semantics assigns computational meaning to valid strings in a programming language syntax. It is closely related to, and often crosses over with, the semantics of mathematical proofs. Semantics describes the processes a computer follows when executing a program in that specific language. This can be done by describing the relationship between the input and output of a program, or giving an explanation of how the program will be executed on a certain platform, thereby creating a model of computation.

History In 1967, Robert W. Floyd published the paper Assigning meanings to programs; his chief aim was "a rigorous standard for proofs about computer programs, including proofs of correctness, equivalence, and termination". Floyd further wrote:

A semantic definition of a programming language, in our approach, is founded on a syntactic definition. It must specify which of the phrases in a syntactically correct program represent commands, and what conditions must be imposed on an interpretation in the neighborhood of each command. In 1969, Tony Hoare published a paper on Hoare logic seeded by Floyd's ideas, now sometimes collectively called axiomatic semantics. In the 1970s, the terms operational semantics and denotational semantics emerged.

Overview The field of formal semantics encompasses all of the following:

The definition of semantic models The relations between different semantic models The relations between different approaches to meaning The relation between computation and the underlying mathematical structures from fields such as logic, set theory, model theory, category theory, etc. It has close links with other areas of computer science such as programming language design, type theory, compilers and interpreters, program verification and model checking.

Approaches There are many approaches to formal semantics; these belong to three major classes:

Denotational semantics, whereby each phrase in the language is interpreted as a denotation, i.e. a conceptual meaning that can be thought of abstractly. Such denotations are often mathematical objects inhabiting a mathematical space, but it is not a requirement that they should be so. As a practical necessity, denotations are described using some form of mathematical notation, which can in turn be formalized as a denotational metalanguage. For example, the denotational semantics of functional languages often translate the language into domain theory. Denotational semantic descriptions can also serve as compositional translations from a programming language into the denotational metalanguage and be used as a basis for designing compilers. Operational semantics, whereby the execution of the language is described directly (rather than by translation). Operational semantics loosely corresponds to interpretation, although again the "implementation language" of the interpreter is generally a mathematical formalism. Operational semantics may define an abstract machine (such as the SECD machine), and give meaning to phrases by describing the transitions they induce on states of the machine. Alternatively, as with the pure lambda calculus, operational semantics can be defined via syntactic transformations on phrases of the language itself; Axiomatic semantics, whereby one gives meaning to phrases by describing the axioms that apply to them. Axiomatic semantics makes no distinction between a phrase's meaning and the logical formulas that describe it; its meaning is exactly what can be proven about it in some logic. The canonical example of axiomatic semantics is Hoare logic. Apart from the choice between denotational, operational, or axiomatic approaches, most variations in formal semantic systems arise from the choice of supporting mathematical formalism.

Variations Some variations of formal semantics include the following:

Action semantics is an approach that tries to modularize denotational semantics, splitting the formalization process in two layers (macro and microsemantics) and predefining three semantic entities (actions, data and yielders) to simplify the specification; Algebraic semantics is a form of axiomatic semantics based on algebraic laws for describing and reasoning about program semantics in a formal manner. It also supports denotational semantics and operational semantics; Attribute grammars define systems that systematically compute "metadata" (called attributes) for the various cases of the language's syntax. Attribute grammars can be understood as a denotational semantics where the target language is simply the original language enriched with attribute annotations. Aside from formal semantics, attribute grammars have also been used for code generation in compilers, and to augment regular or context-free grammars with context-sensitive conditions; Categorical (or "functorial") semantics uses category theory as the core mathematical formalism. Categorical semantics is usually proven to correspond to some axiomatic semantics that gives a syntactic presentation of the categorical structures. Also, denotational semantics are often instances of a general categorical semantics; Concurrency semantics is a catch-all term for any formal semantics that describes concurrent computations. Historically important concurrent formalisms have included the actor model and process calculi; Game semantics uses a metaphor inspired by game theory; Predicate transformer semantics, developed by Edsger W. Dijkstra, describes the meaning of a program fragment as the function transforming a postcondition to the precondition needed to establish it.

Describing relationships For a variety of reasons, one might wish to describe the relationships between different formal semantics. For example:

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Semantics (programming languages)

Start with the simplest possible case. Write down what Semantics (programming languages) claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Semantics (programming languages) before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Semantics (programming languages) ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Semantics (programming languages)

In research
Semantics (programming languages) appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Semantics (programming languages) in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Semantics (programming languages) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Formal methods, Formal specification languages, Logic in computer science, so understanding it makes those chapters shorter.
In everyday life
Look for Semantics (programming languages) outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Semantics (programming languages) in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Semantics (programming languages) means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Semantics (programming languages) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Semantics (programming languages) in simple terms?

In programming language theory, semantics is the rigorous mathematical logic study of the meaning of programming languages. Semantics assigns computational meaning to valid strings in a programming language syntax.

Why does Semantics (programming languages) matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Semantics (programming languages)?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Semantics (programming languages).

Tags

  • Formal methods
  • Formal specification languages
  • Logic in computer science
  • Programming language semantics

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