Semantics with Applications 3. More on Operational Semantics

Size: px
Start display at page:

Download "Semantics with Applications 3. More on Operational Semantics"

Transcription

1 Semantics with Applications 3. More on Operational Semantics Hanne Riis Nielson, Flemming Nielson (thanks to Henrik Pilegaard) [SwA] Hanne Riis Nielson, Flemming Nielson Semantics with Applications: An Appetizer Springer, / 40

2 Recap Natural Semantics Structural Operational Semantics 2 / 40

3 Language While: Syntactic Categories Numerals n Num n ::= 0 1 n0 n1 Variables x Var e.g. x, y, z,... Arithmetic expressions a Aexp a ::= n x a 1 + a 2 a 1 a 2 a 1 a 2 Boolean expressions b Bexp b ::= true false a 1 = a 2 a 1 a 2 b b 1 b 2 Statements S Stm S ::= x := a skip S 1 ; S 2 if b then S 1 else S 2 while b do S 3 / 40

4 Natural semantics for While 4 / 40

5 The Semantic Function for Statements The meaning of statements can be summarised as a partial function from State to State S ns : Stm (State State) Partial function: not necessarily defined for all elements of the domain Definition: { s if S, s s S ns [[S]]s = undef otherwise Need for partiality: non-terminating statements such as while true do skip 5 / 40

6 Induction on the Shape of Derivation Trees In the proof of the previous lemma we were inspecting the structure of the derivation tree for certain transitions This can be generalized to the following proof technique: Prove that the property holds for all the axioms Prove that the property holds for all other rules : Assume that the property holds for its premises (this is called the induction hypothesis, sometimes abbreviated IH) Prove that it holds for the conclusion (provided the side conditions are satisfied) 6 / 40

7 Structural Operational Semantics for While 7 / 40

8 Derivation Sequences A derivation sequence of a statement S starting in state s is either a finite sequence γ0, γ 1,..., γ k γ 0 γ 1... γ k and γ k is final (of the form s) an infinite sequence γ0, γ 1, γ 2,... γ 0 γ 1 γ 2... where γ 0 = S, s and γ i γ i+1 for 0 i(< k) We write γ 0 i γ i to indicate i derivation steps We write γ 0 γ i to indicate finitely many derivation steps 8 / 40

9 The Semantic Function for Statements As we did in the case for Natural Semantics, the meaning of statements can be summarised as a partial function from State to State S sos : Stm (State State) Definition: { s if S, s S sos [[S]]s = s undef otherwise 9 / 40

10 Concepts for NS and SOS The following concepts can be defined both for Natural Semantics and Structural Operational Semantics Termination Looping Semantic equivalence Determinism The formalisations differ however we will compare them in the following 10 / 40

11 Termination Natural Semantics: The execution of S from state s terminates if and only if there is a state s such that S, s s. Structural Operational Semantics: The execution of S from state s terminates if and only if there is a finite derivation sequence starting with S, s, i.e. and γ k is final. S, s γ 1... γ k 11 / 40

12 Looping Natural Semantics: We say that the execution of S from state s loops if and only if there is no state s such that S, s s. Structural Operational Semantics: We say that the execution of S from state s loops if and only if there is an infinite derivation sequence starting with S, s, i.e. S, s γ 1 γ / 40

13 Semantic Equivalence Natural Semantics: Two statements S 1 and S 2 are semantically equivalent if for all states s and s S 1, s s if and only if S 2, s s Structural Operational Semantics: Two statements S 1 and S 2 are semantically equivalent if for all states s: S 1, s γ if and only if S 2, s γ, whenever γ is final there is an infinite derivation sequence starting with S 1, s if and only if there is one starting in S 2, s 13 / 40

14 Determinism Natural Semantics: The semantics is deterministic if for all statements S and states s, s, and s we have that S, s s and S, s s imply s = s Structural Operational Semantics: The semantics is deterministic if for all S and s, γ, and γ we have that S, s γ and S, s γ imply γ = γ 14 / 40

15 Induction on the Length of Derivation Sequences For Structural Operational Semantics it is often useful to conduct proofs by the length of derivation sequences Prove that the property holds for all derivation sequences of length 0 Prove that the property holds for all other derivation sequences Assume that the property holds for all derivation sequences of length at most k (this is called the induction hypothesis) Prove that it holds for derivation sequences of length k+1 15 / 40

16 Three Proof Principles Structural Induction Induction on the Shape of Derivation Trees Especially important for Natural Semantics Induction on the Length of Derivation Sequences Especially important for Structural Operational Semantics 16 / 40

17 Non-sequential Language Extensions Reading material: Section 3.1 of SwA 17 / 40

18 Abortion We extend the syntax of While with the statement abort S ::= x := a skip S 1 ; S 2 if b then S 1 else S 2 while b do S abort Intuition: abort stops the execution of the complete program We model this by ensuring that configurations abort, s are stuck Neither for NS nor for SOS we change the set of transition rules! Then there is no rule to apply when the abort-statement is encountered, ensuring that the respective configuration is stuck. 18 / 40

19 abort NS: no derivation tree Abortion: Three examples SOS: finite derivation sequence abort, s skip NS: derivation tree skip, s s SOS: finite derivation sequence skip, s s Both NS and SOS can establish that abort and skip are not semantically equivalent. 19 / 40

20 abort NS: no derivation tree Abortion: Three examples SOS: finite derivation sequence abort, s skip NS: derivation tree skip, s s SOS: finite derivation sequence skip, s s Both NS and SOS can establish that abort and skip are not semantically equivalent. while true do skip NS: no derivation tree SOS: infinite derivation sequence 20 / 40

21 abort NS: no derivation tree Abortion: Three examples SOS: finite derivation sequence abort, s skip NS: derivation tree skip, s s SOS: finite derivation sequence skip, s s Both NS and SOS can establish that abort and skip are not semantically equivalent. while true do skip NS: no derivation tree SOS: infinite derivation sequence abort and while true do skip are semantically equivalent in NS! SOS however can distinguish between abnormal termination and looping. 21 / 40

22 Abortion Natural semantics: we cannot distinguish between looping and abnormal termination SOS: looping is reflected by infinite derivation sequence and abnormal termination by finite derivation sequence ending in a stuck configuration 22 / 40

23 Non-determinism We extend the syntax of While with the statement or S ::=... S 1 or S 2 Intuition: For S 1 or S 2 we can non-deterministically choose to execute either S 1 or S 2 For both NS and SOS, new rules have to be introduced to handle the statement 23 / 40

24 Rules for non-determinism in Natural Semantics 24 / 40

25 Rules for non-determinism in SOS 25 / 40

26 Non-determinism: Two examples x := 1 or (x := 2; x := x + 2) NS: x := 1 or (x := 2; x := x + 2), s (while true do skip) or (x := 2; x := x + 2) 26 / 40

27 Non-determinism: Two examples x := 1 or (x := 2; x := x + 2) NS: x := 1 or (x := 2; x := x + 2), s s[x 1] (while true do skip) or (x := 2; x := x + 2) 27 / 40

28 Non-determinism: Two examples x := 1 or (x := 2; x := x + 2) NS: x := 1 or (x := 2; x := x + 2), s s[x 1] NS: x := 1 or (x := 2; x := x + 2), s s[x 4] Similar in SOS (while true do skip) or (x := 2; x := x + 2) 28 / 40

29 Non-determinism: Two examples x := 1 or (x := 2; x := x + 2) NS: x := 1 or (x := 2; x := x + 2), s s[x 1] NS: x := 1 or (x := 2; x := x + 2), s s[x 4] Similar in SOS (while true do skip) or (x := 2; x := x + 2) NS: (while true do skip) or (x := 2; x := x + 2), s s[x 4] is the only derivation tree! 29 / 40

30 Non-determinism: Two examples x := 1 or (x := 2; x := x + 2) NS: x := 1 or (x := 2; x := x + 2), s s[x 1] NS: x := 1 or (x := 2; x := x + 2), s s[x 4] Similar in SOS (while true do skip) or (x := 2; x := x + 2) NS: (while true do skip) or (x := 2; x := x + 2), s s[x 4] is the only derivation tree! SOS: Infinite derivation sequence, if the left branch is chosen; finite derivation sequence ending in s[x 4] if the right branch is chosen 30 / 40

31 Non-determinism: Two examples x := 1 or (x := 2; x := x + 2) NS: x := 1 or (x := 2; x := x + 2), s s[x 1] NS: x := 1 or (x := 2; x := x + 2), s s[x 4] Similar in SOS (while true do skip) or (x := 2; x := x + 2) NS: (while true do skip) or (x := 2; x := x + 2), s s[x 4] is the only derivation tree! SOS: Infinite derivation sequence, if the left branch is chosen; finite derivation sequence ending in s[x 4] if the right branch is chosen NS suppresses looping! (Never chooses the wrong branch.) SOS: Looping is not suppressed. 31 / 40

32 Parallelism We extend the syntax of While with the statement par S ::=... S 1 par S 2 Intuition: For S 1 par S 2, both branches have to be executed, but their execution can be interleaved For example: x := 1 par x := 2; x := x / 40

33 Parallelism We extend the syntax of While with the statement par S ::=... S 1 par S 2 Intuition: For S 1 par S 2, both branches have to be executed, but their execution can be interleaved For example: x := 1 par x := 2; x := x + 2 can be executed as either: x := 1; x := 2; x := x + 2 x := 2; x := 1; x := x + 2 x := 2; x := x + 2; x := 1 For SOS new rules can be introduced to handle the statement For NS? 33 / 40

34 Rules for parallelism in SOS 34 / 40

35 An example of parallelism in SOS (1/3) 35 / 40

36 An example of parallelism in SOS (2/3) 36 / 40

37 An example of parallelism in SOS (3/3) 37 / 40

38 Attempt at rules for parallelism in Natural Semantics Unfortunately these are erroneous as they do not express interleaving so Natural Semantics cannot express parallelism 38 / 40

39 Summary: Non-sequential Language Constructs Natural semantics Structural Operational Semantics Non-determinism suppresses looping does not suppress looping Parallelism cannot express interlea- can express interleaving ving of computations of computations 39 / 40

40 Summary Recap Extensions of operational semantics non-determinism, parallelism Exercise Class Exercises 3.1, 3.3, 3,4, 3.5, 3.6 from SwA. 40 / 40

Formal Syntax and Semantics of Programming Languages

Formal Syntax and Semantics of Programming Languages Formal Syntax and Semantics of Programming Languages Mooly Sagiv Reference: Semantics with Applications Chapter 2 H. Nielson and F. Nielson http://www.daimi.au.dk/~bra8130/wiley_book/wiley.html axioms

More information

Formal Syntax and Semantics of Programming Languages

Formal Syntax and Semantics of Programming Languages Formal Syntax and Semantics of Programming Languages Mooly Sagiv Reference: Semantics with Applications Chapter 2 H. Nielson and F. Nielson http://www.daimi.au.dk/~bra8130/wiley_book/wiley.html The While

More information

Exercises on Semantics of Programming Languages

Exercises on Semantics of Programming Languages Technische Universität Wien SS 2014 Fakultät für Informatik Repetition sheet Assist. Prof. Florian Zuleger Tuesday, 8 April 2014 Assist. Prof. Georg Weissenbacher Univ. Prof. Agata Ciabattoni Moritz Sinn,

More information

Formal Semantics of Programming Languages

Formal Semantics of Programming Languages Formal Semantics of Programming Languages Mooly Sagiv Reference: Semantics with Applications Chapter 2 H. Nielson and F. Nielson http://www.daimi.au.dk/~bra8130/wiley_book/wiley.html Benefits of formal

More information

Formal Semantics of Programming Languages

Formal Semantics of Programming Languages Formal Semantics of Programming Languages Mooly Sagiv Reference: Semantics with Applications Chapter 2 H. Nielson and F. Nielson http://www.daimi.au.dk/~bra8130/wiley_book/wiley.html Benefits of formal

More information

PROGRAM ANALYSIS & SYNTHESIS

PROGRAM ANALYSIS & SYNTHESIS Lecture 02 Structural Operational Semantics (SOS) PROGRAM ANALYSIS & SYNTHESIS EranYahav 1 Previously static analysis over-approximation of program behavior abstract interpretation abstraction, transformers,

More information

Program Analysis: Lecture 02 Page 1 of 32

Program Analysis: Lecture 02 Page 1 of 32 Program Analysis: Lecture 02 Page 1 of 32 Program Analysis/ Mooly Sagiv Lecture 1, 31/10/2012 Operational Semantics Notes by: Kalev Alpernas As background to the subject of Program Analysis, we will first

More information

Formal Semantics of Programming Languages

Formal Semantics of Programming Languages Formal Semantics of Programming Languages Mooly Sagiv Reference: Semantics with Applications Chapter 2 H. Nielson and F. Nielson http://www.daimi.au.dk/~bra8130/wiley_book/wiley.html Benefits of formal

More information

Application: Programming Language Semantics

Application: Programming Language Semantics Chapter 8 Application: Programming Language Semantics Prof. Dr. K. Madlener: Specification and Verification in Higher Order Logic 527 Introduction to Programming Language Semantics Programming Language

More information

Big-step Operational Semantics (aka Natural Semantics)

Big-step Operational Semantics (aka Natural Semantics) x = 1 let x = 1 in... x(1).!x(1) x.set(1) Programming Language Theory Big-step Operational Semantics (aka Natural Semantics) Ralf Lämmel A big-step operational semantics for While 2 This slide is derived

More information

Induction and Semantics in Dafny

Induction and Semantics in Dafny 15-414 Lecture 11 1 Instructor: Matt Fredrikson Induction and Semantics in Dafny TA: Ryan Wagner Encoding the syntax of Imp Recall the abstract syntax of Imp: a AExp ::= n Z x Var a 1 + a 2 b BExp ::=

More information

Note that in this definition, n + m denotes the syntactic expression with three symbols n, +, and m, not to the number that is the sum of n and m.

Note that in this definition, n + m denotes the syntactic expression with three symbols n, +, and m, not to the number that is the sum of n and m. CS 6110 S18 Lecture 8 Structural Operational Semantics and IMP Today we introduce a very simple imperative language, IMP, along with two systems of rules for evaluation called small-step and big-step semantics.

More information

Static Program Analysis

Static Program Analysis Static Program Analysis Lecture 1: Introduction to Program Analysis Thomas Noll Lehrstuhl für Informatik 2 (Software Modeling and Verification) noll@cs.rwth-aachen.de http://moves.rwth-aachen.de/teaching/ws-1415/spa/

More information

Formal semantics of loosely typed languages. Joep Verkoelen Vincent Driessen

Formal semantics of loosely typed languages. Joep Verkoelen Vincent Driessen Formal semantics of loosely typed languages Joep Verkoelen Vincent Driessen June, 2004 ii Contents 1 Introduction 3 2 Syntax 5 2.1 Formalities.............................. 5 2.2 Example language LooselyWhile.................

More information

Static Program Analysis

Static Program Analysis Static Program Analysis Thomas Noll Software Modeling and Verification Group RWTH Aachen University https://moves.rwth-aachen.de/teaching/ss-18/spa/ Preliminaries Outline of Lecture 1 Preliminaries Introduction

More information

3.7 Denotational Semantics

3.7 Denotational Semantics 3.7 Denotational Semantics Denotational semantics, also known as fixed-point semantics, associates to each programming language construct a well-defined and rigorously understood mathematical object. These

More information

Goals: Define the syntax of a simple imperative language Define a semantics using natural deduction 1

Goals: Define the syntax of a simple imperative language Define a semantics using natural deduction 1 Natural Semantics Goals: Define the syntax of a simple imperative language Define a semantics using natural deduction 1 1 Natural deduction is an instance of first-order logic; that is, it is the formal

More information

2 Introduction to operational semantics

2 Introduction to operational semantics 2 Introduction to operational semantics This chapter presents the syntax of a programming language, IMP, a small language of while programs. IMP is called an "imperative" language because program execution

More information

Harvard School of Engineering and Applied Sciences CS 152: Programming Languages

Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Lecture 19 Tuesday, April 3, 2018 1 Introduction to axiomatic semantics The idea in axiomatic semantics is to give specifications

More information

CS4215 Programming Language Implementation. Martin Henz

CS4215 Programming Language Implementation. Martin Henz CS4215 Programming Language Implementation Martin Henz Thursday 26 January, 2012 2 Chapter 4 The Language simpl In this chapter, we are exting the language epl in order to provide a more powerful programming

More information

Propositional Logic Formal Syntax and Semantics. Computability and Logic

Propositional Logic Formal Syntax and Semantics. Computability and Logic Propositional Logic Formal Syntax and Semantics Computability and Logic Syntax and Semantics Syntax: The study of how expressions are structured (think: grammar) Semantics: The study of the relationship

More information

Lecture 5 - Axiomatic semantics

Lecture 5 - Axiomatic semantics Program Verification March 2014 Lecture 5 - Axiomatic semantics Lecturer: Noam Rinetzky Scribes by: Nir Hemed 1.1 Axiomatic semantics The development of the theory is contributed to Robert Floyd, C.A.R

More information

Chapter 3. The While programming language

Chapter 3. The While programming language Chapter 3 The While programming language 1 Contents 3 The While programming language 1 3.1 Big-step semantics........................... 2 3.2 Small-step semantics.......................... 9 3.3 Properties................................

More information

Semantics via Syntax. f (4) = if define f (x) =2 x + 55.

Semantics via Syntax. f (4) = if define f (x) =2 x + 55. 1 Semantics via Syntax The specification of a programming language starts with its syntax. As every programmer knows, the syntax of a language comes in the shape of a variant of a BNF (Backus-Naur Form)

More information

Principles of Programming Languages

Principles of Programming Languages (An Introduction to... ) Principles of Programming Languages Adrian Francalanza Department of Computer Science ICT, University of Malta 2009/10 There is usually two parts to the definition of a (programming)

More information

axiomatic semantics involving logical rules for deriving relations between preconditions and postconditions.

axiomatic semantics involving logical rules for deriving relations between preconditions and postconditions. CS 6110 S18 Lecture 18 Denotational Semantics 1 What is Denotational Semantics? So far we have looked at operational semantics involving rules for state transitions, definitional semantics involving translations

More information

CMSC 336: Type Systems for Programming Languages Lecture 5: Simply Typed Lambda Calculus Acar & Ahmed January 24, 2008

CMSC 336: Type Systems for Programming Languages Lecture 5: Simply Typed Lambda Calculus Acar & Ahmed January 24, 2008 CMSC 336: Type Systems for Programming Languages Lecture 5: Simply Typed Lambda Calculus Acar & Ahmed January 24, 2008 Contents 1 Solution to the Exercise 1 1.1 Semantics for lambda calculus.......................

More information

Verifying Safety Property of Lustre Programs: Temporal Induction

Verifying Safety Property of Lustre Programs: Temporal Induction 22c181: Formal Methods in Software Engineering The University of Iowa Spring 2008 Verifying Safety Property of Lustre Programs: Temporal Induction Copyright 2008 Cesare Tinelli. These notes are copyrighted

More information

AXIOMS OF AN IMPERATIVE LANGUAGE PARTIAL CORRECTNESS WEAK AND STRONG CONDITIONS. THE AXIOM FOR nop

AXIOMS OF AN IMPERATIVE LANGUAGE PARTIAL CORRECTNESS WEAK AND STRONG CONDITIONS. THE AXIOM FOR nop AXIOMS OF AN IMPERATIVE LANGUAGE We will use the same language, with the same abstract syntax that we used for operational semantics. However, we will only be concerned with the commands, since the language

More information

Semantics. There is no single widely acceptable notation or formalism for describing semantics Operational Semantics

Semantics. There is no single widely acceptable notation or formalism for describing semantics Operational Semantics There is no single widely acceptable notation or formalism for describing semantics Operational Describe the meaning of a program by executing its statements on a machine, either simulated or actual. The

More information

CS422 - Programming Language Design

CS422 - Programming Language Design 1 CS422 - Programming Language Design Denotational Semantics Grigore Roşu Department of Computer Science University of Illinois at Urbana-Champaign 2 Denotational semantics, alsoknownasfix-point semantics,

More information

Programming Languages Fall 2014

Programming Languages Fall 2014 Programming Languages Fall 2014 Lecture 7: Simple Types and Simply-Typed Lambda Calculus Prof. Liang Huang huang@qc.cs.cuny.edu 1 Types stuck terms? how to fix it? 2 Plan First I For today, we ll go back

More information

Data Flow Analysis using Program Graphs

Data Flow Analysis using Program Graphs Data Flow Analysis using Program Graphs Michal Terepeta Supervised by Professor Hanne Riis Nielson Professor Andrzej Napieralski Kongens Lyngby 2010 Technical University of Denmark Informatics and Mathematical

More information

arxiv: v1 [cs.pl] 10 May 2016

arxiv: v1 [cs.pl] 10 May 2016 Flag-Based Big-Step Semantics Casper Bach Poulsen 1,, Peter D. Mosses Department of Computer Science Swansea University Singleton Park, Swansea SA2 8PP, UK arxiv:1605.02935v1 [cs.pl] 10 May 2016 Abstract

More information

More Untyped Lambda Calculus & Simply Typed Lambda Calculus

More Untyped Lambda Calculus & Simply Typed Lambda Calculus Concepts in Programming Languages Recitation 6: More Untyped Lambda Calculus & Simply Typed Lambda Calculus Oded Padon & Mooly Sagiv (original slides by Kathleen Fisher, John Mitchell, Shachar Itzhaky,

More information

CIS 500 Software Foundations. Final Exam. May 3, Answer key

CIS 500 Software Foundations. Final Exam. May 3, Answer key CIS 500 Software Foundations Final Exam May 3, 2012 Answer key This exam includes material on the Imp language and the simply-typed lambda calculus. Some of the key definitions are repeated, for easy reference,

More information

1. Abstract syntax: deep structure and binding. 2. Static semantics: typing rules. 3. Dynamic semantics: execution rules.

1. Abstract syntax: deep structure and binding. 2. Static semantics: typing rules. 3. Dynamic semantics: execution rules. Introduction Formal definitions of programming languages have three parts: CMPSCI 630: Programming Languages Static and Dynamic Semantics Spring 2009 (with thanks to Robert Harper) 1. Abstract syntax:

More information

Programming Languages Fall 2013

Programming Languages Fall 2013 Programming Languages Fall 2013 Lecture 3: Induction Prof. Liang Huang huang@qc.cs.cuny.edu Recursive Data Types (trees) data Ast = ANum Integer APlus Ast Ast ATimes Ast Ast eval (ANum x) = x eval (ATimes

More information

Mosig M1 - PLSCD Written exam

Mosig M1 - PLSCD Written exam Mosig M1 - PLSCD 0809 - Written exam 1 Exercise I : Operational semantics - a debugger In this exercise we consider program execution with the help of a debugger. This execution will be defined on an intermediate

More information

6. Hoare Logic and Weakest Preconditions

6. Hoare Logic and Weakest Preconditions 6. Hoare Logic and Weakest Preconditions Program Verification ETH Zurich, Spring Semester 07 Alexander J. Summers 30 Program Correctness There are many notions of correctness properties for a given program

More information

Introduction to Denotational Semantics. Class Likes/Dislikes Survey. Dueling Semantics. Denotational Semantics Learning Goals. You re On Jeopardy!

Introduction to Denotational Semantics. Class Likes/Dislikes Survey. Dueling Semantics. Denotational Semantics Learning Goals. You re On Jeopardy! Introduction to Denotational Semantics Class Likes/Dislikes Survey would change [the bijection question] to be one that still tested students' recollection of set theory but that didn't take as much time

More information

Handout 9: Imperative Programs and State

Handout 9: Imperative Programs and State 06-02552 Princ. of Progr. Languages (and Extended ) The University of Birmingham Spring Semester 2016-17 School of Computer Science c Uday Reddy2016-17 Handout 9: Imperative Programs and State Imperative

More information

CSE 505, Fall 2008, Midterm Examination 29 October Please do not turn the page until everyone is ready.

CSE 505, Fall 2008, Midterm Examination 29 October Please do not turn the page until everyone is ready. CSE 505, Fall 2008, Midterm Examination 29 October 2008 Please do not turn the page until everyone is ready. Rules: The exam is closed-book, closed-note, except for one side of one 8.5x11in piece of paper.

More information

Foundations of Computer Science Spring Mathematical Preliminaries

Foundations of Computer Science Spring Mathematical Preliminaries Foundations of Computer Science Spring 2017 Equivalence Relation, Recursive Definition, and Mathematical Induction Mathematical Preliminaries Mohammad Ashiqur Rahman Department of Computer Science College

More information

Intro to semantics; Small-step semantics Lecture 1 Tuesday, January 29, 2013

Intro to semantics; Small-step semantics Lecture 1 Tuesday, January 29, 2013 Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Lecture 1 Tuesday, January 29, 2013 1 Intro to semantics What is the meaning of a program? When we write a program, we use

More information

Principles of Program Analysis: A Sampler of Approaches

Principles of Program Analysis: A Sampler of Approaches Principles of Program Analysis: A Sampler of Approaches Transparencies based on Chapter 1 of the book: Flemming Nielson, Hanne Riis Nielson and Chris Hankin: Principles of Program Analysis Springer Verlag

More information

Programming Languages Third Edition

Programming Languages Third Edition Programming Languages Third Edition Chapter 12 Formal Semantics Objectives Become familiar with a sample small language for the purpose of semantic specification Understand operational semantics Understand

More information

02157 Functional Programming Interpreters for two simple languages including exercises

02157 Functional Programming Interpreters for two simple languages including exercises Interpreters for two simple languages including exercises nsen 1 DTU Informatics, Technical University of Denmark Purpose To show the power of a functional programming language, we present a prototype

More information

Abstract Interpretation

Abstract Interpretation Abstract Interpretation Ranjit Jhala, UC San Diego April 22, 2013 Fundamental Challenge of Program Analysis How to infer (loop) invariants? Fundamental Challenge of Program Analysis Key issue for any analysis

More information

Overview. Probabilistic Programming. Dijkstra s guarded command language: Syntax. Elementary pgcl ingredients. Lecture #4: Probabilistic GCL

Overview. Probabilistic Programming. Dijkstra s guarded command language: Syntax. Elementary pgcl ingredients. Lecture #4: Probabilistic GCL Overview Lecture #4: Probabilistic GCL 1 Joost-Pieter Katoen 2 3 Recursion RWTH Lecture Series on 2018 Joost-Pieter Katoen 1/31 Joost-Pieter Katoen 2/31 Dijkstra s guarded command language: Syntax Elementary

More information

3.2 Big-Step Structural Operational Semantics (Big-Step SOS)

3.2 Big-Step Structural Operational Semantics (Big-Step SOS) 3.2 Big-Step Structural Operational Semantics (Big-Step SOS) Known also under the names natural semantics, relational semantics, and evaluation semantics, big-step structural operational semantics, or

More information

A CRASH COURSE IN SEMANTICS

A CRASH COURSE IN SEMANTICS LAST TIME Recdef More induction NICTA Advanced Course Well founded orders Slide 1 Theorem Proving Principles, Techniques, Applications Slide 3 Well founded recursion Calculations: also/finally {P}... {Q}

More information

Denotational Semantics

Denotational Semantics Denotational Semantics 8 12 lectures for Part II CST 2010/11 Marcelo Fiore Course web page: http://www.cl.cam.ac.uk/teaching/1011/denotsem/ 1 Lecture 1 Introduction 2 What is this course about? General

More information

Thursday, December 23, The attack model: Static Program Analysis

Thursday, December 23, The attack model: Static Program Analysis The attack model: Static Program Analysis How making SPA? DFA - Data Flow Analysis CFA - Control Flow Analysis Proving invariance: theorem proving Checking models: model checking Giaco & Ranzato DFA:

More information

Lecture 9: Typed Lambda Calculus

Lecture 9: Typed Lambda Calculus Advanced Topics in Programming Languages Spring Semester, 2012 Lecture 9: Typed Lambda Calculus May 8, 2012 Lecturer: Mooly Sagiv Scribe: Guy Golan Gueta and Shachar Itzhaky 1 Defining a Type System for

More information

Semantics. A. Demers Jan This material is primarily from Ch. 2 of the text. We present an imperative

Semantics. A. Demers Jan This material is primarily from Ch. 2 of the text. We present an imperative CS411 Notes 1: IMP and Large Step Operational Semantics A. Demers 23-25 Jan 2001 This material is primarily from Ch. 2 of the text. We present an imperative language called IMP; wegive a formal definition

More information

Chapter 3: Propositional Languages

Chapter 3: Propositional Languages Chapter 3: Propositional Languages We define here a general notion of a propositional language. We show how to obtain, as specific cases, various languages for propositional classical logic and some non-classical

More information

From IMP to Java. Andreas Lochbihler. parts based on work by Gerwin Klein and Tobias Nipkow ETH Zurich

From IMP to Java. Andreas Lochbihler. parts based on work by Gerwin Klein and Tobias Nipkow ETH Zurich From IMP to Java Andreas Lochbihler ETH Zurich parts based on work by Gerwin Klein and Tobias Nipkow 2015-07-14 1 Subtyping 2 Objects and Inheritance 3 Multithreading 1 Subtyping 2 Objects and Inheritance

More information

Lesson 4 Typed Arithmetic Typed Lambda Calculus

Lesson 4 Typed Arithmetic Typed Lambda Calculus Lesson 4 Typed Arithmetic Typed Lambda 1/28/03 Chapters 8, 9, 10 Outline Types for Arithmetic types the typing relation safety = progress + preservation The simply typed lambda calculus Function types

More information

Mutable References. Chapter 1

Mutable References. Chapter 1 Chapter 1 Mutable References In the (typed or untyped) λ-calculus, or in pure functional languages, a variable is immutable in that once bound to a value as the result of a substitution, its contents never

More information

Semantics of COW. July Alex van Oostenrijk and Martijn van Beek

Semantics of COW. July Alex van Oostenrijk and Martijn van Beek Semantics of COW /; ;\ \\ // /{_\_/ `'\ \ (o) (o } / :--',-,'`@@@@@@@@ @@@@@@ \_ ` \ ;:( @@@@@@@@@ @@@ \ (o'o) :: ) @@@@ @@@@@@,'@@( `====' Moo! :: : @@@@@: @@@@ `@@@: :: \ @@@@@: @@@@@@@) ( '@@@' ;; /\

More information

Proving Properties on Programs From the Coq Tutorial at ITP 2015

Proving Properties on Programs From the Coq Tutorial at ITP 2015 Proving Properties on Programs From the Coq Tutorial at ITP 2015 Reynald Affeldt August 29, 2015 Hoare logic is a proof system to verify imperative programs. It consists of a language of Hoare triples

More information

COMP 382: Reasoning about algorithms

COMP 382: Reasoning about algorithms Spring 2015 Unit 2: Models of computation What is an algorithm? So far... An inductively defined function Limitation Doesn t capture mutation of data Imperative models of computation Computation = sequence

More information

CIS 500 Software Foundations Fall October 2

CIS 500 Software Foundations Fall October 2 CIS 500 Software Foundations Fall 2006 October 2 Preliminaries Homework Results of my email survey: There was one badly misdesigned (PhD) problem and a couple of others that were less well thought through

More information

From OCL to Propositional and First-order Logic: Part I

From OCL to Propositional and First-order Logic: Part I 22c181: Formal Methods in Software Engineering The University of Iowa Spring 2008 From OCL to Propositional and First-order Logic: Part I Copyright 2007-8 Reiner Hähnle and Cesare Tinelli. Notes originally

More information

Homework 6: Big- & small-step semantics for LC

Homework 6: Big- & small-step semantics for LC Homework 6: Big- & small-step semantics for LC CIS 352: Programming Languages 23 February 2018, Version 2 Administrivia Typo corrections in red. Trade ideas with another student? Document it in your source

More information

CS2104 Prog. Lang. Concepts

CS2104 Prog. Lang. Concepts CS2104 Prog. Lang. Concepts Operational Semantics Abhik Roychoudhury Department of Computer Science National University of Singapore Organization An imperative language IMP Formalizing the syntax of IMP

More information

CMSC 330: Organization of Programming Languages. Formal Semantics of a Prog. Lang. Specifying Syntax, Semantics

CMSC 330: Organization of Programming Languages. Formal Semantics of a Prog. Lang. Specifying Syntax, Semantics Recall Architecture of Compilers, Interpreters CMSC 330: Organization of Programming Languages Source Scanner Parser Static Analyzer Operational Semantics Intermediate Representation Front End Back End

More information

Introduction to Denotational Semantics. Brutus Is An Honorable Man. Class Likes/Dislikes Survey. Dueling Semantics

Introduction to Denotational Semantics. Brutus Is An Honorable Man. Class Likes/Dislikes Survey. Dueling Semantics Brutus Is An Honorable Man HW2 will not be due today. Homework X+1 will never be due until after I have returned Homework X to you. Normally this is never an issue, but I was sick yesterday and was hosting

More information

COSC252: Programming Languages: Semantic Specification. Jeremy Bolton, PhD Adjunct Professor

COSC252: Programming Languages: Semantic Specification. Jeremy Bolton, PhD Adjunct Professor COSC252: Programming Languages: Semantic Specification Jeremy Bolton, PhD Adjunct Professor Outline I. What happens after syntactic analysis (parsing)? II. Attribute Grammars: bridging the gap III. Semantic

More information

Lecture Note: Types. 1 Introduction 2. 2 Simple Types 3. 3 Type Soundness 6. 4 Recursive Types Subtyping 17

Lecture Note: Types. 1 Introduction 2. 2 Simple Types 3. 3 Type Soundness 6. 4 Recursive Types Subtyping 17 Jens Palsberg Sep 24, 1999 Contents Lecture Note: Types 1 Introduction 2 2 Simple Types 3 3 Type Soundness 6 4 Recursive Types 12 5 Subtyping 17 6 Decision Procedure for Subtyping 19 7 First-Order Unification

More information

The exercises and answer for programming language class

The exercises and answer for programming language class The exercises and answer for programming language class Neversay: g9403@cherry.cs.nccu.edu.tw March 14, 2006 Abstract There are exercises and its answer in detail. Please read it carefully. This exercise

More information

CIS 500 Software Foundations Midterm I

CIS 500 Software Foundations Midterm I CIS 500 Software Foundations Midterm I October 11, 2006 Name: Student ID: Email: Status: Section: registered for the course not registered: sitting in to improve a previous grade not registered: just taking

More information

09 B: Graph Algorithms II

09 B: Graph Algorithms II Correctness and Complexity of 09 B: Graph Algorithms II CS1102S: Data Structures and Algorithms Martin Henz March 19, 2010 Generated on Thursday 18 th March, 2010, 00:20 CS1102S: Data Structures and Algorithms

More information

Theory of Languages and Automata

Theory of Languages and Automata Theory of Languages and Automata Chapter 3- The Church-Turing Thesis Sharif University of Technology Turing Machine O Several models of computing devices Finite automata Pushdown automata O Tasks that

More information

Semantics of Programming Languages

Semantics of Programming Languages P Lecture Notes on Semantics of Programming Languages for Part IB of the Computer Science Tripos Andrew M. Pitts University of Cambridge Computer Laboratory c A. M. Pitts, 1997-2002 First edition 1997.

More information

Type Safety. Java and ML are type safe, or strongly typed, languages. C and C++ are often described as weakly typed languages.

Type Safety. Java and ML are type safe, or strongly typed, languages. C and C++ are often described as weakly typed languages. Java and ML are type safe, or strongly typed, languages. CMPSCI 630: Programming Languages Spring 2009 (with thanks to Robert Harper) C and C++ are often described as weakly typed languages. What does

More information

Lambda Calculus. Type Systems, Lectures 3. Jevgeni Kabanov Tartu,

Lambda Calculus. Type Systems, Lectures 3. Jevgeni Kabanov Tartu, Lambda Calculus Type Systems, Lectures 3 Jevgeni Kabanov Tartu, 13.02.2006 PREVIOUSLY ON TYPE SYSTEMS Arithmetical expressions and Booleans Evaluation semantics Normal forms & Values Getting stuck Safety

More information

Foundations: Syntax, Semantics, and Graphs

Foundations: Syntax, Semantics, and Graphs Foundations: Syntax, Semantics, and Graphs Testing, Quality Assurance, and Maintenance Winter 2018 Prof. Arie Gurfinkel based on slides by Ruzica Pizkac, Claire Le Goues, Lin Tan, Marsha Chechik, and others

More information

Deriving a Slicing Algorithm via FermaT Transformations

Deriving a Slicing Algorithm via FermaT Transformations Deriving a Slicing Algorithm via FermaT Transformations M. P. Ward and H. Zedan Software Technology Research Lab De Montfort University The Gateway, Leicester LE1 9BH, UK martin@gkc.org.uk and zedan@dmu.ac.uk

More information

Lecture Notes on Program Equivalence

Lecture Notes on Program Equivalence Lecture Notes on Program Equivalence 15-312: Foundations of Programming Languages Frank Pfenning Lecture 24 November 30, 2004 When are two programs equal? Without much reflection one might say that two

More information

Finite Automata Theory and Formal Languages TMV027/DIT321 LP4 2018

Finite Automata Theory and Formal Languages TMV027/DIT321 LP4 2018 Finite Automata Theory and Formal Languages TMV027/DIT321 LP4 2018 Lecture 11 Ana Bove April 26th 2018 Recap: Regular Languages Decision properties of RL: Is it empty? Does it contain this word? Contains

More information

3.4 Denotational Semantics

3.4 Denotational Semantics 3.4 Denotational Semantics Denotational semantics, also known as fixed-point semantics, associates to each syntactically welldefined fragment of program a well-defined, rigorous mathematical object. This

More information

In Our Last Exciting Episode

In Our Last Exciting Episode In Our Last Exciting Episode #1 Lessons From Model Checking To find bugs, we need specifications What are some good specifications? To convert a program into a model, we need predicates/invariants and

More information

Part II. Hoare Logic and Program Verification. Why specify programs? Specification and Verification. Code Verification. Why verify programs?

Part II. Hoare Logic and Program Verification. Why specify programs? Specification and Verification. Code Verification. Why verify programs? Part II. Hoare Logic and Program Verification Part II. Hoare Logic and Program Verification Dilian Gurov Props: Models: Specs: Method: Tool: safety of data manipulation source code logic assertions Hoare

More information

Lambda Calculus: Implementation Techniques and a Proof. COS 441 Slides 15

Lambda Calculus: Implementation Techniques and a Proof. COS 441 Slides 15 Lambda Calculus: Implementation Techniques and a Proof COS 441 Slides 15 Last Time: The Lambda Calculus A language of pure functions: values e ::= x \x.e e e v ::= \x.e With a call-by-value operational

More information

Principles of AI Planning. Principles of AI Planning. 7.1 How to obtain a heuristic. 7.2 Relaxed planning tasks. 7.1 How to obtain a heuristic

Principles of AI Planning. Principles of AI Planning. 7.1 How to obtain a heuristic. 7.2 Relaxed planning tasks. 7.1 How to obtain a heuristic Principles of AI Planning June 8th, 2010 7. Planning as search: relaxed planning tasks Principles of AI Planning 7. Planning as search: relaxed planning tasks Malte Helmert and Bernhard Nebel 7.1 How to

More information

Subsumption. Principle of safe substitution

Subsumption. Principle of safe substitution Recap on Subtyping Subsumption Some types are better than others, in the sense that a value of one can always safely be used where a value of the other is expected. Which can be formalized as by introducing:

More information

Toward Abstract Interpretation of Program Transformations

Toward Abstract Interpretation of Program Transformations Abstract Toward Abstract Interpretation of Program Transformations Developers of program transformations often reason about transformations to assert certain properties of the generated code. We propose

More information

4.5 Pure Linear Functional Programming

4.5 Pure Linear Functional Programming 4.5 Pure Linear Functional Programming 99 4.5 Pure Linear Functional Programming The linear λ-calculus developed in the preceding sections can serve as the basis for a programming language. The step from

More information

COMP 507: Computer-Aided Program Design

COMP 507: Computer-Aided Program Design Fall 2014 April 7, 2015 Goal: Correctness proofs Prove that an algorithm written in an imperative language is correct Induction for algorithmic correctness Induction for functional programs: The program

More information

CIS 500: Software Foundations

CIS 500: Software Foundations CIS 500: Software Foundations Midterm II November 9, 2017 Name (printed): Username (PennKey login id): My signature below certifies that I have complied with the University of Pennsylvania s Code of Academic

More information

CS 125 Section #4 RAMs and TMs 9/27/16

CS 125 Section #4 RAMs and TMs 9/27/16 CS 125 Section #4 RAMs and TMs 9/27/16 1 RAM A word-ram consists of: A fixed set of instructions P 1,..., P q. Allowed instructions are: Modular arithmetic and integer division on registers; the standard

More information

CONVENTIONAL EXECUTABLE SEMANTICS. Grigore Rosu CS422 Programming Language Design

CONVENTIONAL EXECUTABLE SEMANTICS. Grigore Rosu CS422 Programming Language Design CONVENTIONAL EXECUTABLE SEMANTICS Grigore Rosu CS422 Programming Language Design Conventional Semantic Approaches A language designer should understand the existing design approaches, techniques and tools,

More information

Introduction to Axiomatic Semantics

Introduction to Axiomatic Semantics Introduction to Axiomatic Semantics Meeting 10, CSCI 5535, Spring 2009 Announcements Homework 3 due tonight Homework 2 is graded 13 (mean), 14 (median), out of 21 total, but Graduate class: final project

More information

Programming Languages

Programming Languages CSE 230: Winter 2008 Principles of Programming Languages Ocaml/HW #3 Q-A Session Push deadline = Mar 10 Session Mon 3pm? Lecture 15: Type Systems Ranjit Jhala UC San Diego Why Typed Languages? Development

More information

Quiz 1: Solutions J/18.400J: Automata, Computability and Complexity. Nati Srebro, Susan Hohenberger

Quiz 1: Solutions J/18.400J: Automata, Computability and Complexity. Nati Srebro, Susan Hohenberger 6.45J/8.4J: utomata, Computability and Complexity Quiz : Solutions Prof. Nancy Lynch Nati Srebro, Susan Hohenberger Please write your name in the upper corner of each page. (2 Points) Q- Problem : True

More information

Static Program Analysis

Static Program Analysis Static Program Analysis Thomas Noll Software Modeling and Verification Group RWTH Aachen University https://moves.rwth-aachen.de/teaching/ws-1617/spa/ Schedule of Lectures Jan 17/19: Interprocedural DFA

More information

CS5371 Theory of Computation. Lecture 8: Automata Theory VI (PDA, PDA = CFG)

CS5371 Theory of Computation. Lecture 8: Automata Theory VI (PDA, PDA = CFG) CS5371 Theory of Computation Lecture 8: Automata Theory VI (PDA, PDA = CFG) Objectives Introduce Pushdown Automaton (PDA) Show that PDA = CFG In terms of descriptive power Pushdown Automaton (PDA) Roughly

More information

The Rule of Constancy(Derived Frame Rule)

The Rule of Constancy(Derived Frame Rule) The Rule of Constancy(Derived Frame Rule) The following derived rule is used on the next slide The rule of constancy {P } C {Q} {P R} C {Q R} where no variable assigned to in C occurs in R Outline of derivation

More information