Mutation Testing of Functional Programming Languages
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Processing an Intermediate Representation Written in Lisp
Processing an Intermediate Representation Written in Lisp Ricardo Pena,˜ Santiago Saavedra, Jaime Sanchez-Hern´ andez´ Complutense University of Madrid, Spain∗ [email protected],[email protected],[email protected] In the context of designing the verification platform CAVI-ART, we arrived to the need of deciding a textual format for our intermediate representation of programs. After considering several options, we finally decided to use S-expressions for that textual representation, and Common Lisp for processing it in order to obtain the verification conditions. In this paper, we discuss the benefits of this decision. S-expressions are homoiconic, i.e. they can be both considered as data and as code. We exploit this duality, and extensively use the facilities of the Common Lisp environment to make different processing with these textual representations. In particular, using a common compilation scheme we show that program execution, and verification condition generation, can be seen as two instantiations of the same generic process. 1 Introduction Building a verification platform such as CAVI-ART [7] is a challenging endeavour from many points of view. Some of the tasks require intensive research which can make advance the state of the art, as it is for instance the case of automatic invariant synthesis. Some others are not so challenging, but require a lot of pondering and discussion before arriving at a decision, in order to ensure that all the requirements have been taken into account. A bad or a too quick decision may have a profound influence in the rest of the activities, by doing them either more difficult, or longer, or more inefficient. -
Rubicon: Bounded Verification of Web Applications
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by DSpace@MIT Rubicon: Bounded Verification of Web Applications Joseph P. Near, Daniel Jackson Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge, MA, USA {jnear,dnj}@csail.mit.edu ABSTRACT ification language is an extension of the Ruby-based RSpec We present Rubicon, an application of lightweight formal domain-specific language for testing [7]; Rubicon adds the methods to web programming. Rubicon provides an embed- quantifiers of first-order logic, allowing programmers to re- ded domain-specific language for writing formal specifica- place RSpec tests over a set of mock objects with general tions of web applications and performs automatic bounded specifications over all objects. This compatibility with the checking that those specifications hold. Rubicon's language existing RSpec language makes it easy for programmers al- is based on the RSpec testing framework, and is designed to ready familiar with testing to write specifications, and to be both powerful and familiar to programmers experienced convert existing RSpec tests into specifications. in testing. Rubicon's analysis leverages the standard Ruby interpreter to perform symbolic execution, generating veri- Rubicon's automated analysis comprises two parts: first, fication conditions that Rubicon discharges using the Alloy Rubicon generates verification conditions based on specifica- Analyzer. We have tested Rubicon's scalability on five real- tions; second, Rubicon invokes a constraint solver to check world applications, and found a previously unknown secu- those conditions. The Rubicon library modifies the envi- rity bug in Fat Free CRM, a popular customer relationship ronment so that executing a specification performs symbolic management system. -
Exploring Languages with Interpreters and Functional Programming Chapter 8
Exploring Languages with Interpreters and Functional Programming Chapter 8 H. Conrad Cunningham 24 September 2018 Contents 8 Evaluation Model 2 8.1 Chapter Introduction . .2 8.2 Referential Transparency Revisited . .2 8.3 Substitution Model . .3 8.4 Time and Space Complexity . .6 8.5 Termination . .7 8.6 What Next? . .7 8.7 Exercises . .8 8.8 Acknowledgements . .8 8.9 References . .9 8.10 Terms and Concepts . .9 Copyright (C) 2016, 2017, 2018, H. Conrad Cunningham Professor of Computer and Information Science University of Mississippi 211 Weir Hall P.O. Box 1848 University, MS 38677 (662) 915-5358 Browser Advisory: The HTML version of this textbook requires a browser that supports the display of MathML. A good choice as of September 2018 is a recent version of Firefox from Mozilla. 1 8 Evaluation Model 8.1 Chapter Introduction This chapter introduces an evaluation model applicable to Haskell programs. As in the previous chapters, this chapter focuses on use of first-order functions and primitive data types. A goal of this chapter and the next one is enable students to analyze Haskell functions to determine under what conditions they terminate normally and how efficient they are. This chapter presents the evaluation model and the next chapter informally analyzes simple functions in terms of time and space efficiency and termination. How can we evaluate (i.e. execute) an expression that “calls” a function like the fact1 function from Chapter 4? We do this by rewriting expressions using a substitution model, as we see in this chapter. This process depends upon a property of functional languages called referential transparency. -
Week 3: Scope and Evaluation Order Assignment 1 Has Been Posted!
Week 3: Scope and Evaluation Order CSC324 Principles of Programming Languages David Liu, Department of Computer Science Assignment 1 has been posted! Closures and static scope static (adjective) determined only by the program source code dynamic (adjective) determined only when the program is run E.g., referential transparency is a static property How are closures implemented? Are they static or dynamic? Or both? Function bodies can be processed statically (e.g., Haskell compiler generates code once per lambda). The closure environment (and therefore the closure itself) can only be generated dynamically. The closure depends only on where the function is evaluated, not where that function is called. (define (make-adder n) (lambda (x) (+ x n))) (define adder (make-adder 1)) (adder 100) So we can determine where each free identifier obtains its values statically, based on where its enclosing function is defined. scope (of an identifier) The parts of program code that may refer to that identifier. static (aka lexical) scope The scope of every identifier is determined by the structure of the source code (e.g., by nesting of lambdas and lets). Every identifier obtains its value from the closest enclosing expression that binds it. (define (make-adder n) (lambda (x) (+ x n))) (define adder (make-adder 1)) ; (0x..., {n: 1}) (let* ([n 100]) (adder 2)) Implementing static scope in an interpreter A simple interpreter (define/match (interpret env expr) [(_ (? number?)) expr] [(_ (? symbol?)) (hash-ref env expr)] [(_ (list '+ l r)) (+ (interpret env l) (interpret env r))]) A simple interpreter (define/match (interpret env expr) [(_ (? number?)) expr] [(_ (? symbol?)) (hash-ref env expr)] [(_ (list '+ l r)) (+ (interpret env l) (interpret env r))]) The environment is passed recursively when interpreting each subexpression. -
SI 413, Unit 3: Advanced Scheme
SI 413, Unit 3: Advanced Scheme Daniel S. Roche ([email protected]) Fall 2018 1 Pure Functional Programming Readings for this section: PLP, Sections 10.7 and 10.8 Remember there are two important characteristics of a “pure” functional programming language: • Referential Transparency. This fancy term just means that, for any expression in our program, the result of evaluating it will always be the same. In fact, any referentially transparent expression could be replaced with its value (that is, the result of evaluating it) without changing the program whatsoever. Notice that imperative programming is about as far away from this as possible. For example, consider the C++ for loop: for ( int i = 0; i < 10;++i) { /∗ some s t u f f ∗/ } What can we say about the “stuff” in the body of the loop? Well, it had better not be referentially transparent. If it is, then there’s no point in running over it 10 times! • Functions are First Class. Another way of saying this is that functions are data, just like any number or list. Functions are values, in fact! The specific privileges that a function earns by virtue of being first class include: 1) Functions can be given names. This is not a big deal; we can name functions in pretty much every programming language. In Scheme this just means we can do (define (f x) (∗ x 3 ) ) 2) Functions can be arguments to other functions. This is what you started to get into at the end of Lab 2. For starters, there’s the basic predicate procedure?: (procedure? +) ; #t (procedure? 10) ; #f (procedure? procedure?) ; #t 1 And then there are “higher-order functions” like map and apply: (apply max (list 5 3 10 4)) ; 10 (map sqrt (list 16 9 64)) ; '(4 3 8) What makes the functions “higher-order” is that one of their arguments is itself another function. -
HOL-Testgen Version 1.8 USER GUIDE
HOL-TestGen Version 1.8 USER GUIDE Achim Brucker, Lukas Brügger, Abderrahmane Feliachi, Chantal Keller, Matthias Krieger, Delphine Longuet, Yakoub Nemouchi, Frédéric Tuong, Burkhart Wolff To cite this version: Achim Brucker, Lukas Brügger, Abderrahmane Feliachi, Chantal Keller, Matthias Krieger, et al.. HOL-TestGen Version 1.8 USER GUIDE. [Technical Report] Univeristé Paris-Saclay; LRI - CNRS, University Paris-Sud. 2016. hal-01765526 HAL Id: hal-01765526 https://hal.inria.fr/hal-01765526 Submitted on 12 Apr 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. R L R I A P P O R HOL-TestGen Version 1.8 USER GUIDE T BRUCKER A D / BRUGGER L / FELIACHI A / KELLER C / KRIEGER M P / LONGUET D / NEMOUCHI Y / TUONG F / WOLFF B D Unité Mixte de Recherche 8623 E CNRS-Université Paris Sud-LRI 04/2016 R Rapport de Recherche N° 1586 E C H E R C CNRS – Université de Paris Sud H Centre d’Orsay LABORATOIRE DE RECHERCHE EN INFORMATIQUE E Bâtiment 650 91405 ORSAY Cedex (France) HOL-TestGen 1.8.0 User Guide http://www.brucker.ch/projects/hol-testgen/ Achim D. Brucker Lukas Brügger Abderrahmane Feliachi Chantal Keller Matthias P. -
Exploring Languages with Interpreters and Functional Programming Chapter 11
Exploring Languages with Interpreters and Functional Programming Chapter 11 H. Conrad Cunningham 24 January 2019 Contents 11 Software Testing Concepts 2 11.1 Chapter Introduction . .2 11.2 Software Requirements Specification . .2 11.3 What is Software Testing? . .3 11.4 Goals of Testing . .3 11.5 Dimensions of Testing . .3 11.5.1 Testing levels . .4 11.5.2 Testing methods . .6 11.5.2.1 Black-box testing . .6 11.5.2.2 White-box testing . .8 11.5.2.3 Gray-box testing . .9 11.5.2.4 Ad hoc testing . .9 11.5.3 Testing types . .9 11.5.4 Combining levels, methods, and types . 10 11.6 Aside: Test-Driven Development . 10 11.7 Principles for Test Automation . 12 11.8 What Next? . 15 11.9 Exercises . 15 11.10Acknowledgements . 15 11.11References . 16 11.12Terms and Concepts . 17 Copyright (C) 2018, H. Conrad Cunningham Professor of Computer and Information Science University of Mississippi 211 Weir Hall P.O. Box 1848 University, MS 38677 1 (662) 915-5358 Browser Advisory: The HTML version of this textbook requires a browser that supports the display of MathML. A good choice as of October 2018 is a recent version of Firefox from Mozilla. 2 11 Software Testing Concepts 11.1 Chapter Introduction The goal of this chapter is to survey the important concepts, terminology, and techniques of software testing in general. The next chapter illustrates these techniques by manually constructing test scripts for Haskell functions and modules. 11.2 Software Requirements Specification The purpose of a software development project is to meet particular needs and expectations of the project’s stakeholders. -
Beginner's Luck
Beginner’s Luck A Language for Property-Based Generators Leonidas Lampropoulos1 Diane Gallois-Wong2,3 Cat˘ alin˘ Hrit¸cu2 John Hughes4 Benjamin C. Pierce1 Li-yao Xia2,3 1University of Pennsylvania, USA 2INRIA Paris, France 3ENS Paris, France 4Chalmers University, Sweden Abstract An appealing feature of QuickCheck is that it offers a library Property-based random testing a` la QuickCheck requires build- of property combinators resembling standard logical operators. For ing efficient generators for well-distributed random data satisfying example, a property of the form p ==> q, built using the impli- complex logical predicates, but writing these generators can be dif- cation combinator ==>, will be tested automatically by generating ficult and error prone. We propose a domain-specific language in valuations (assignments of random values, of appropriate type, to which generators are conveniently expressed by decorating pred- the free variables of p and q), discarding those valuations that fail icates with lightweight annotations to control both the distribu- to satisfy p, and checking whether any of the ones that remain are tion of generated values and the amount of constraint solving that counterexamples to q. happens before each variable is instantiated. This language, called QuickCheck users soon learn that this default generate-and-test Luck, makes generators easier to write, read, and maintain. approach sometimes does not give satisfactory results. In particular, We give Luck a formal semantics and prove several fundamen- if the precondition p is satisfied by relatively few values of the ap- tal properties, including the soundness and completeness of random propriate type, then most of the random inputs that QuickCheck generation with respect to a standard predicate semantics. -
Quickcheck Tutorial
QuickCheck Tutorial Thomas Arts John Hughes Quviq AB Queues Erlang contains a queue data structure (see stdlib documentation) We want to test that these queues behave as expected What is “expected” behaviour? We have a mental model of queues that the software should conform to. Erlang Exchange 2008 © Quviq AB 2 Queue Mental model of a fifo queue …… first last …… Remove from head Insert at tail / rear Erlang Exchange 2008 © Quviq AB 3 Queue Traditional test cases could look like: We want to check for arbitrary elements that Q0 = queue:new(), if we add an element, Q1 = queue:cons(1,Q0), it's there. 1 = queue:last(Q1). Q0 = queue:new(), We want to check for Q1 = queue:cons(8,Q0), arbitrary queues that last added element is Q2 = queue:cons(0,Q1), "last" 0 = queue:last(Q2), Property is like an abstraction of a test case Erlang Exchange 2008 © Quviq AB 4 QuickCheck property We want to know that for any element, when we add it, it's there prop_itsthere() -> ?FORALL(I,int(), I == queue:last( queue:cons(I, queue:new()))). Erlang Exchange 2008 © Quviq AB 5 QuickCheck property We want to know that for any element, when we add it, it's there prop_itsthere() -> ?FORALL(I,int(), I == queue:last( queue:cons(I, queue:new()))). This is a property Test cases are generasted from such properties Erlang Exchange 2008 © Quviq AB 6 QuickCheck property We want to know that for any element, when we add it, it's there int() is a generator for random integers prop_itsthere() -> ?FORALL(I,int(), I == queue:last( queue:cons(I, I represents one queue:new()))). -
Branching Processes for Quickcheck Generators (Extended Version)
Branching Processes for QuickCheck Generators (extended version) Agustín Mista Alejandro Russo John Hughes Universidad Nacional de Rosario Chalmers University of Technology Chalmers University of Technology Rosario, Argentina Gothenburg, Sweden Gothenburg, Sweden [email protected] [email protected] [email protected] Abstract random test data generators or QuickCheck generators for short. In QuickCheck (or, more generally, random testing), it is chal- The generation of test cases is guided by the types involved in lenging to control random data generators’ distributions— the testing properties. It defines default generators for many specially when it comes to user-defined algebraic data types built-in types like booleans, integers, and lists. However, when (ADT). In this paper, we adapt results from an area of mathe- it comes to user-defined ADTs, developers are usually required matics known as branching processes, and show how they help to specify the generation process. The difficulty is, however, to analytically predict (at compile-time) the expected number that it might become intricate to define generators so that they of generated constructors, even in the presence of mutually result in a suitable distribution or enforce data invariants. recursive or composite ADTs. Using our probabilistic formu- The state-of-the-art tools to derive generators for user- las, we design heuristics capable of automatically adjusting defined ADTs can be classified based on the automation level probabilities in order to synthesize generators which distribu- as well as the sort of invariants enforced at the data genera- tions are aligned with users’ demands. We provide a Haskell tion phase. QuickCheck and SmallCheck [27] (a tool for writing implementation of our mechanism in a tool called generators that synthesize small test cases) use type-driven and perform case studies with real-world applications. -
Foundational Property-Based Testing
Foundational Property-Based Testing Zoe Paraskevopoulou1,2 Cat˘ alin˘ Hrit¸cu1 Maxime Den´ es` 1 Leonidas Lampropoulos3 Benjamin C. Pierce3 1Inria Paris-Rocquencourt 2ENS Cachan 3University of Pennsylvania Abstract Integrating property-based testing with a proof assistant creates an interesting opportunity: reusable or tricky testing code can be formally verified using the proof assistant itself. In this work we introduce a novel methodology for formally verified property-based testing and implement it as a foundational verification framework for QuickChick, a port of QuickCheck to Coq. Our frame- work enables one to verify that the executable testing code is testing the right Coq property. To make verification tractable, we provide a systematic way for reasoning about the set of outcomes a random data generator can produce with non-zero probability, while abstracting away from the actual probabilities. Our framework is firmly grounded in a fully verified implementation of QuickChick itself, using the same underlying verification methodology. We also apply this methodology to a complex case study on testing an information-flow control abstract machine, demonstrating that our verification methodology is modular and scalable and that it requires minimal changes to existing code. 1 Introduction Property-based testing (PBT) allows programmers to capture informal conjectures about their code as executable specifications and to thoroughly test these conjectures on a large number of inputs, usually randomly generated. When a counterexample is found it is shrunk to a minimal one, which is displayed to the programmer. The original Haskell QuickCheck [19], the first popular PBT tool, has inspired ports to every mainstream programming language and led to a growing body of continuing research [6,18,26,35,39] and industrial interest [2,33]. -
Specification Based Testing with Quickcheck
Specification Based Testing with QuickCheck (Tutorial Talk) John Hughes Chalmers University of Technology and QuviQ AB ABSTRACT QuickCheck is a tool which tests software against a formal specification, reporting discrepancies as minimal failing examples. QuickCheck uses properties specified by the developer both to generate test cases, and to identify failing tests. A property such as 8xs : list(int()): reverse(reverse(xs)) = xs is tested by generating random lists of integers, binding them to the variable xs, then evaluating the boolean expression under the quantifier and reporting a failure if the value is false. If a failing test case is found, QuickCheck “shrinks” it by searching for smaller, but similar test cases that also fail, terminating with a minimal example that cannot be shrunk further. In this example, if the developer accidentally wrote 8xs : list(int()): reverse(xs) = xs instead, then QuickCheck would report the list [0; 1] as the minimal failing case, containing as few list elements as possible, with the smallest absolute values possible. The approach is very practical: QuickCheck is implemented just a library in a host programming language; it needs only to execute the code under test, so requires no tools other than a compiler (in particular, no static analysis); the shrinking process “extracts the signal from the noise” of random testing, and usually results in very easy-to-debug failures. First developed in Haskell by Koen Claessen and myself, QuickCheck has been emulated in many programming languages, and in 2006 I founded QuviQ to develop and market an Erlang version. Of course, customers’ code is much more complex than the simple reverse function above, and requires much more complex properties to test it.