DOCSLIB.ORG

Quickcheck - Random Property-Based Testing

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Quickcheck - Random Property-Based Testing QuickCheck - Random Property-based Testing Arnold Schwaighofer QuickCheck - Random Property-based Haskell - A Truly (Cool) Functional Programming Testing Language Why Functional Programming And In Particular Quickcheck - A Truly Cool QuickCheck Is Cool Property Based Testing Tool Summary Custom Random Arnold Schwaighofer Data Generators Resources Institute for Formal Models and Verification References Johannes Kepler University Linz 26 June 2007 / KV Debugging QuickCheck - Random Outline Property-based Testing Arnold Schwaighofer Haskell - A Truly (Cool) Functional Programming Language Haskell - A Truly (Cool) Functional What - Functional Programming Programming Language How - Functional Programming Quickcheck - A Why - Functional Programming Truly Cool Property Based Testing Tool Quickcheck - A Truly Cool Property Based Testing Tool Summary Custom Random What - Random Property Based Testing Data Generators How - Random Property Based Testing Resources Why - Random Property Based Testing References Success Stories Related Work And Outlook Summary QuickCheck - Random Outline Property-based Testing Arnold Schwaighofer Haskell - A Truly (Cool) Functional Programming Language Haskell - A Truly (Cool) Functional What - Functional Programming Programming Language How - Functional Programming Quickcheck - A Why - Functional Programming Truly Cool Property Based Testing Tool Quickcheck - A Truly Cool Property Based Testing Tool Summary Custom Random What - Random Property Based Testing Data Generators How - Random Property Based Testing Resources Why - Random Property Based Testing References Success Stories Related Work And Outlook Summary QuickCheck - Random Outline Property-based Testing Arnold Schwaighofer Haskell - A Truly (Cool) Functional Programming Language Haskell - A Truly (Cool) Functional What - Functional Programming Programming Language How - Functional Programming Quickcheck - A Why - Functional Programming Truly Cool Property Based Testing Tool Quickcheck - A Truly Cool Property Based Testing Tool Summary Custom Random What - Random Property Based Testing Data Generators How - Random Property Based Testing Resources Why - Random Property Based Testing References Success Stories Related Work And Outlook Summary QuickCheck - Random What Is Functional Programming? Property-based Testing From a lazy perspective Arnold Schwaighofer Haskell - A Truly As in features of the programming language Haskell (Cool) Functional Programming [JH99]. Language What - Functional Functions are first class - values that can be passed Programming I How - Functional Programming around Why - Functional Programming I Referential integrity - no side effects! Quickcheck - A Truly Cool I Pattern matching - Write functions according to the Property Based Testing Tool type’s data constructor Summary I Laziness - evaluate terms when they are needed Custom Random Data Generators (and only once) Resources I Statically typed - all terms must have a valid type at References compile time QuickCheck - Random Functions As Essential Building Blocks Property-based Testing Functions are curried Arnold Schwaighofer multiply :: Integer → Integer → Integer Haskell - A Truly multiply x y = x ∗ y (Cool) Functional Programming Language Functions build of other functions What - Functional Programming How - Functional Programming multiplyByTwo :: Integer → Integer Why - Functional Programming multiplyByTwo x = multiply 2 x Quickcheck - A Truly Cool Property Based Functions can be polymorphic Testing Tool Summary id :: a → a Custom Random Data Generators id x = x Resources References applyTwice :: (a → a) → a → a applyTwice f x = f (f x) file:///Users/arnold/Desktop/qc-ex/01-func.hs QuickCheck - Random A Repeating Pattern Property-based Testing Arnold Functions are defined using pattern matching on the Schwaighofer argument type(s). Haskell - A Truly (Cool) Functional Programming data List a = Empty | Prepend a (List a) Language What - Functional List a ≡ [a] Programming How - Functional Empty ≡ [] Programming Why - Functional Prepend x xs ≡ x : xs Programming Quickcheck - A Truly Cool Property Based Testing Tool [1, 2, 3, 4] ≡ 1 :(2 :(3 :(4 : [ ]))) Summary Custom Random Data Generators len :: [a] → Integer Resources len [ ] = 0 References len (x : xs) = 1 + len xs file:///Users/arnold/Desktop/qc-ex/02-pattern.hs QuickCheck - Random The Beauty Of Being Lazy Property-based Testing Functions are lazy. Only evaluate when result is needed. Arnold Schwaighofer allNumbersFrom :: Integer → [Integer ] Haskell - A Truly allNumbersFrom x = x : allNumbersFrom (x + 1) (Cool) Functional Programming Language What - Functional first :: [Integer ] → Integer Programming How - Functional first [ ] = [ ] Programming Why - Functional Programming first (x : xs) = x Quickcheck - A Truly Cool Property Based Testing Tool take :: Integer → [a] → [a] Summary take 0 = [ ] Custom Random take [ ] = [ ] Data Generators take n (x : xs) = x :(take (n − 1) xs) Resources References first (allNumbersFrom 1) ≡ 1 take 5 (allNumbersFrom 1) ≡ [1, 2, 3, 4, 5] file:///Users/arnold/Desktop/qc-ex/03-lazy.hs QuickCheck - Random Advantages Of Functional Programming Property-based Testing Arnold Schwaighofer Haskell - A Truly I Modular programming - higher order functions, (Cool) Functional Programming producer consumer pattern due to laziness [Hug89] Language What - Functional Programming I Conciseness - less to write, less to read How - Functional Programming I Easier to debug - because functions are pure Why - Functional Programming I Easier to test - because functions are pure Quickcheck - A Truly Cool Property Based I Safer - Type checking finds a lot of errors before Testing Tool even running the program [Car97] Summary Typed Lambda Calculus [Chu36]- mathematical Custom Random I Data Generators theory more beautiful than Turing Machine (to me at Resources least) References QuickCheck - Random QuickCheck [CH00] What Is It About? Property-based Testing Arnold Schwaighofer Haskell - A Truly I Traditionally test cases are written by hand (Unit (Cool) Functional Programming testing) Language Quickcheck - A I Tedious work - remember we are in a lazy setting Truly Cool Property Based I Idea: Functional setting, dependency only on Testing Tool What - Random Property arguments Based Testing How - Random Property Based Testing I Specify properties of a function (e.g f (x) > 0 for all x) Why - Random Property Based Testing Randomly generate arguments and check that Success Stories I Related Work And Outlook property holds Summary We can do this even for arguments that are functions Custom Random I Data Generators (remember Haskell is cool) Resources References QuickCheck - Random Property Based Testing Property-based Testing Arnold Schwaighofer Test case Function Haskell - A Truly (Cool) Functional generator under test Programming Language Quickcheck - A Truly Cool Property Based Testing Tool What - Random Property Based Testing How - Random Property Based Testing Why - Random Property Based Testing Success Stories Property Related Work And Outlook Summary Custom Random Data Generators Resources OK? [Cla06] References QuickCheck - Random Simple Tests Property-based Testing Random test generators for most build-in types (Integers, Arnold Schwaighofer Boolean, Tuples, Lists) are predefined. Haskell - A Truly (Cool) Functional prop_RevUnit :: Integer → Bool Programming Language prop_RevUnit x = Quickcheck - A reverse [x ] ≡ [x ] Truly Cool Property Based Testing Tool What - Random Property Based Testing prop_RevRev :: [Integer ] → Bool How - Random Property Based Testing Why - Random Property prop_RevRev xs = Based Testing Success Stories reverse (reverse xs) ≡ xs Related Work And Outlook Summary Custom Random Data Generators prop_RevApp :: [Integer ] → [Integer ] → Bool Resources prop_RevApp xs ys = References reverse (xs ++ ys) ≡ reverse ys ++ reverse xs file:///Users/arnold/Desktop/qc-ex/04-qcsimple.hs QuickCheck - Random We Don’t Stop At Functions ’Cause Property-based Testing Remember We Are Cool Arnold Schwaighofer Haskell - A Truly Extensionality on functions. (Cool) Functional Programming Language (f === g) x = f x ≡ g x Quickcheck - A Truly Cool Property Based To show function composition is associative. Testing Tool What - Random Property Based Testing How - Random Property prop_CompositionAssociative :: (Int → Int) → Based Testing Why - Random Property (Int → Int) → Based Testing Success Stories (Int → Int) → Related Work And Outlook Summary Int → Bool Custom Random prop_CompositionAssociative f g h = Data Generators f ◦ (g ◦ h) === (f ◦ g) ◦ h Resources References file:///Users/arnold/Desktop/qc-ex/05-qcfunc.hs QuickCheck - Random Observing What Is Going On Property-based Testing There are combinators that can be used in specifications Arnold Schwaighofer that tell us what is going on. Haskell - A Truly (Cool) Functional prop_Insert :: Int → [Int ] → Property Programming Language prop_Insert x xs = Quickcheck - A ordered xs ==> Truly Cool Property Based collect (length xs) \$ Testing Tool What - Random Property ordered (insert x xs) Based Testing How - Random Property Based Testing Why - Random Property Based Testing OK, passed 100 tests. Success Stories Related Work And Outlook 20% 0. Summary 10% 1. Custom Random Data Generators 9% 3. Resources ... References 1% 16. file:///Users/arnold/Desktop/qc-ex/06-monitor.hs QuickCheck - Random Generating Random Data Property-based Testing Arnold Schwaighofer Haskell - A Truly (Cool) Functional Programming Language QuickCheck provides support for user defined random Quickcheck
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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()))).
    [Show full text]
  • 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.
    [Show full text]
  • 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].
    [Show full text]
  • 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.
    [Show full text]
  • Webdriver: Controlling Your Web Browser
    WebDriver: Controlling your Web Browser Erlang User Conference 2013 Hans Svensson, Quviq AB [email protected] First, a confession... I have a confession to make... I have built a web system! In PHP! ... and it was painfully mundane to test It is all forgotten and forgiven... It was back in 2003! First DEMO DEMO A bit of history Proving program Erlang correctness Reality – a constant issue PhD Testing is studentPhD necessary A bit of history Proving program Erlang correctness Reality – a constant issue PhD Testing is studentPhD necessary • We like to write our properties in QuickCheck • How can we control ‘a browser’ from Erlang? • Doing it all from scratch seems hard, unnecessary, stupid, ... Selenium “Selenium automates browsers. That's it. What you do with that power is entirely up to you. Primarily it is for automating web applications for testing purposes, but is certainly not limited to just that. Boring web-based administration tasks can (and should!) also be automated as well.” Selenium • Basically you can create and run scripts • Supported by a wide range of browsers and operating systems • Run tests in: Chrome, IE, Opera, Firefox, and partial support also for other browsers. • Runs on: Windows, OS X, Linux, Solaris, and others. • Script recording using Selenium IDE (Firefox plugin). • Language support for: C#, Java, Python, Ruby, and partial support for Perl and PHP. • Widely used to create Unit-tests and regression testing suites for web services. Selenium 2 - WebDriver • In version 2, Selenium introduced the WebDriver API • Via WebDriver it is possible to drive the browser natively • The browser can be local or remote – possible to use a grid test • It is a compact Object Oriented API • Supports Chrome, Firefox, HtmlUnit, Opera, IE, and IPhone and Android • Languages implementing driver: C#, Java, Python, and Ruby.
    [Show full text]
  • Experience Report: the Next 1100 Haskell Programmers
    Experience Report: The Next 1100 Haskell Programmers Jasmin Christian Blanchette Lars Hupel Tobias Nipkow Lars Noschinski Dmitriy Traytel Fakultat¨ fur¨ Informatik, Technische Universitat¨ Munchen,¨ Germany Abstract mathematics, and linear algebra. The information systems students We report on our experience teaching a Haskell-based functional had only had a basic calculus course and were taking discrete programming course to over 1100 students for two winter terms. mathematics in parallel. The dramatis personae in addition to the students were lecturer The syllabus was organized around selected material from various 1 sources. Throughout the terms, we emphasized correctness through Tobias Nipkow, who designed the course, produced the slides, QuickCheck tests and proofs by induction. The submission archi- and gave the lectures; Masters of TAs Lars Noschinski and Lars tecture was coupled with automatic testing, giving students the pos- Hupel, who directed a dozen teaching assistants (TAs) and took sibility to correct mistakes before the deadline. To motivate the stu- care of the overall organization; furthermore the (Co)Masters of dents, we complemented the weekly assignments with an informal Competition Jasmin Blanchette (MC) and Dmitriy Traytel (CoMC), competition and gave away trophies in a award ceremony. who selected competition problems and ranked the solutions. Categories and Subject Descriptors D.1.1 [Programming Tech- 2. Syllabus niques]: Applicative (Functional) Programming; D.3.2 [Program- ming Languages]: Language Classifications—Applicative (func- The second iteration covered the following topics in order. (The tional) languages; K.3.2 [Computers and Education]: Computer first iteration was similar, with a few exceptions discussed below.) and Information Science Education—Computer science education Each topic was the subject of one 90-minute lecture unless other- wise specified.
    [Show full text]
  • Hypothesis: a New Approach to Property-Based Testing
    Hypothesis: A new approach to property-based testing David R. MacIver1, Zac Hatfield-Dodds2, and many other contributors3 1 Imperial College London 2 Australian National University 3 Various DOI: 10.21105/joss.01891 Software • Review Summary • Repository • Archive Property-based testing is a style of testing popularised by the QuickCheck family of libraries, first in Haskell (Claessen & Hughes, 2000) and later in Erlang (Arts, Hughes, Johansson,& Editor: Daniel S. Katz Wiger, 2006), which integrates generated test cases into existing software testing workflows: Instead of tests that provide examples of a single concrete behaviour, tests specify properties Reviewers: that hold for a wide range of inputs, which the testing library then attempts to generate test • @luizirber cases to refute. For a general introduction to property-based testing, see (MacIver, 2019). • @djmitche Hypothesis is a mature and widely used property-based testing library for Python. It has over Submitted: 12 November 2019 100,000 downloads per week1, thousands of open source projects use it2, and in 2018 more Published: 21 November 2019 than 4% of Python users surveyed by the PSF reported using it3. It will be of interest both to License researchers using Python for developing scientific software, and to software testing researchers Authors of papers retain as a platform for research in its own right. copyright and release the work under a Creative Commons Attribution 4.0 International Hypothesis for Testing Scientific Software License (CC-BY). Python has a rich and thriving ecosystem of scientific software, and Hypothesis is helpful for ensuring its correctness. Any researcher who tests their software in Python can benefit from these facilities, but it is particularly useful for improving the correctness foundational libraries on which the scientific software ecosystem is built.
    [Show full text]