Exploring Languages with Interpreters and Functional Programming Chapter 11
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Smoke Testing
International Journal of Scientific and Research Publications, Volume 4, Issue 2, February 2014 1 ISSN 2250-3153 Smoke Testing Vinod Kumar Chauhan Quality Assurance (QA), Impetus InfoTech Pvt. Ltd. (India) Abstract- Smoke testing is an end-to-end testing which determine the stability of new build by checking the crucial functionality of the application under test and used as criteria of accepting the new build for detailed testing. Index Terms- Software testing, acceptance testing, agile, build, regression testing, sanity testing I. INTRODUCTION The purpose of this research paper is to give details of smoke testing from the industry practices. II. SMOKE TESTING The purpose of smoke testing is to determine whether the new software build is stable or not so that the build could be used for detailed testing by the QA team and further work by the development team. If the build is stable i.e. the smoke test passes then build could be used by the QA and development team. On the stable build QA team performs functional testing for the newly added features/functionality and then performs regression testing depending upon the situation. But if the build is not stable i.e. the smoke fails then the build is rejected to the development team to fix the build issues and create a new build. Smoke Testing is generally done by the QA team but in certain situations, can be done by the development team. In that case the development team checks the stability of the build and deploy to QA only if the build is stable. In other case, whenever there is new build deployment to the QA environment then the team first performs the smoke test on the build and depending upon the results of smoke the decision to accept or reject the build is taken. -
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. -
Types of Software Testing
Types of Software Testing We would be glad to have feedback from you. Drop us a line, whether it is a comment, a question, a work proposition or just a hello. You can use either the form below or the contact details on the rightt. Contact details [email protected] +91 811 386 5000 1 Software testing is the way of assessing a software product to distinguish contrasts between given information and expected result. Additionally, to evaluate the characteristic of a product. The testing process evaluates the quality of the software. You know what testing does. No need to explain further. But, are you aware of types of testing. It’s indeed a sea. But before we get to the types, let’s have a look at the standards that needs to be maintained. Standards of Testing The entire test should meet the user prerequisites. Exhaustive testing isn’t conceivable. As we require the ideal quantity of testing in view of the risk evaluation of the application. The entire test to be directed ought to be arranged before executing it. It follows 80/20 rule which expresses that 80% of defects originates from 20% of program parts. Start testing with little parts and extend it to broad components. Software testers know about the different sorts of Software Testing. In this article, we have incorporated majorly all types of software testing which testers, developers, and QA reams more often use in their everyday testing life. Let’s understand them!!! Black box Testing The black box testing is a category of strategy that disregards the interior component of the framework and spotlights on the output created against any input and performance of the system. -
Smoke Testing What Is Smoke Testing?
Smoke Testing What is Smoke Testing? Smoke testing is the initial testing process exercised to check whether the software under test is ready/stable for further testing. The term ‘Smoke Testing’ is came from the hardware testing, in the hardware testing initial pass is done to check if it did not catch the fire or smoked in the initial switch on.Prior to start Smoke testing few test cases need to created once to use for smoke testing. These test cases are executed prior to start actual testing to checkcritical functionalities of the program is working fine. This set of test cases written such a way that all functionality is verified but not in deep. The objective is not to perform exhaustive testing, the tester need check the navigation’s & adding simple things, tester needs to ask simple questions “Can tester able to access software application?”, “Does user navigates from one window to other?”, “Check that the GUI is responsive” etc. Here are graphical representation of Smoke testing & Sanity testing in software testing: Smoke Sanity Testing Diagram The test cases can be executed manually or automated; this depends upon the project requirements. In this types of testing mainly focus on the important functionality of application, tester do not care about detailed testing of each software component, this can be cover in the further testing of application. The Smoke testing is typically executed by testers after every build is received for checking the build is in testable condition. This type of testing is applicable in the Integration Testing, System Testing and Acceptance Testing levels. -
Comparison of GUI Testing Tools for Android Applications
Comparison of GUI testing tools for Android applications University of Oulu Department of Information Processing Science Master’s Thesis Tomi Lämsä Date 22.5.2017 2 Abstract Test automation is an intriguing area of software engineering, especially in Android development. This is since Android applications must be able to run in many different permutations of operating system versions and hardware choices. Comparison of different tools for automated UI testing of Android applications is done in this thesis. In a literature review several different tools available and their popularity is researched and the structure of the most popular tools is looked at. The two tools identified to be the most popular are Appium and Espresso. In an empirical study the two tools along with Robotium, UiAutomator and Tau are compared against each other in test execution speed, maintainability of the test code, reliability of the test tools and in general issues. An empirical study was carried out by selecting three Android applications for which an identical suite of tests was developed with each tool. The test suites were then run and the execution speed and reliability was analysed based on these results. The test code written is also analysed for maintainability by calculating the lines of code and the number of method calls needed to handle asynchrony related to UI updates. The issues faced by the test developer with the different tools are also analysed. This thesis aims to help industry users of these kinds of applications in two ways. First, it could be used as a source on what tools are over all available for UI testing of Android applications. -
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. -
Opportunities and Open Problems for Static and Dynamic Program Analysis Mark Harman∗, Peter O’Hearn∗ ∗Facebook London and University College London, UK
1 From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis Mark Harman∗, Peter O’Hearn∗ ∗Facebook London and University College London, UK Abstract—This paper1 describes some of the challenges and research questions that target the most productive intersection opportunities when deploying static and dynamic analysis at we have yet witnessed: that between exciting, intellectually scale, drawing on the authors’ experience with the Infer and challenging science, and real-world deployment impact. Sapienz Technologies at Facebook, each of which started life as a research-led start-up that was subsequently deployed at scale, Many industrialists have perhaps tended to regard it unlikely impacting billions of people worldwide. that much academic work will prove relevant to their most The paper identifies open problems that have yet to receive pressing industrial concerns. On the other hand, it is not significant attention from the scientific community, yet which uncommon for academic and scientific researchers to believe have potential for profound real world impact, formulating these that most of the problems faced by industrialists are either as research questions that, we believe, are ripe for exploration and that would make excellent topics for research projects. boring, tedious or scientifically uninteresting. This sociological phenomenon has led to a great deal of miscommunication between the academic and industrial sectors. I. INTRODUCTION We hope that we can make a small contribution by focusing on the intersection of challenging and interesting scientific How do we transition research on static and dynamic problems with pressing industrial deployment needs. Our aim analysis techniques from the testing and verification research is to move the debate beyond relatively unhelpful observations communities to industrial practice? Many have asked this we have typically encountered in, for example, conference question, and others related to it. -
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. -
Software Testing: Essential Phase of SDLC and a Comparative Study Of
International Journal of System and Software Engineering Volume 5 Issue 2, December 2017 ISSN.: 2321-6107 Software Testing: Essential Phase of SDLC and a Comparative Study of Software Testing Techniques Sushma Malik Assistant Professor, Institute of Innovation in Technology and Management, Janak Puri, New Delhi, India. Email: [email protected] Abstract: Software Development Life-Cycle (SDLC) follows In the software development process, the problem (Software) the different activities that are used in the development of a can be dividing in the following activities [3]: software product. SDLC is also called the software process ∑ Understanding the problem and it is the lifeline of any Software Development Model. ∑ Decide a plan for the solution Software Processes decide the survival of a particular software development model in the market as well as in ∑ Coding for the designed solution software organization and Software testing is a process of ∑ Testing the definite program finding software bugs while executing a program so that we get the zero defect software. The main objective of software These activities may be very complex for large systems. So, testing is to evaluating the competence and usability of a each of the activity has to be broken into smaller sub-activities software. Software testing is an important part of the SDLC or steps. These steps are then handled effectively to produce a because through software testing getting the quality of the software project or system. The basic steps involved in software software. Lots of advancements have been done through project development are: various verification techniques, but still we need software to 1) Requirement Analysis and Specification: The goal of be fully tested before handed to the customer. -
Software Testing Training Module
MAST MARKET ALIGNED SKILLS TRAINING SOFTWARE TESTING TRAINING MODULE In partnership with Supported by: INDIA: 1003-1005,DLF City Court, MG Road, Gurgaon 122002 Tel (91) 124 4551850 Fax (91) 124 4551888 NEW YORK: 216 E.45th Street, 7th Floor, New York, NY 10017 www.aif.org SOFTWARE TESTING TRAINING MODULE About the American India Foundation The American India Foundation is committed to catalyzing social and economic change in India, andbuilding a lasting bridge between the United States and India through high impact interventions ineducation, livelihoods, public health, and leadership development. Working closely with localcommunities, AIF partners with NGOs to develop and test innovative solutions and withgovernments to create and scale sustainable impact. Founded in 2001 at the initiative of PresidentBill Clinton following a suggestion from Indian Prime Minister Vajpayee, AIF has impacted the lives of 4.6million of India’s poor. Learn more at www.AIF.org About the Market Aligned Skills Training (MAST) program Market Aligned Skills Training (MAST) provides unemployed young people with a comprehensive skillstraining that equips them with the knowledge and skills needed to secure employment and succeed on thejob. MAST not only meets the growing demands of the diversifying local industries across the country, itharnesses India's youth population to become powerful engines of the economy. AIF Team: Hanumant Rawat, Aamir Aijaz & Rowena Kay Mascarenhas American India Foundation 10th Floor, DLF City Court, MG Road, Near Sikanderpur Metro Station, Gurgaon 122002 216 E. 45th Street, 7th Floor New York, NY 10017 530 Lytton Avenue, Palo Alto, CA 9430 This document is created for the use of underprivileged youth under American India Foundation’s Market Aligned Skills Training (MAST) Program. -
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()))).