Correctness and Execution of Concurrent Object-Oriented Programs
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Effectiveness of Software Testing Techniques in Enterprise: a Case Study
MYKOLAS ROMERIS UNIVERSITY BUSINESS AND MEDIA SCHOOL BRIGITA JAZUKEVIČIŪTĖ (Business Informatics) EFFECTIVENESS OF SOFTWARE TESTING TECHNIQUES IN ENTERPRISE: A CASE STUDY Master Thesis Supervisor – Assoc. Prof. Andrej Vlasenko Vilnius, 2016 CONTENTS INTRODUCTION .................................................................................................................................. 7 1. THE RELATIONSHIP BETWEEN SOFTWARE TESTING AND SOFTWARE QUALITY ASSURANCE ........................................................................................................................................ 11 1.1. Introduction to Software Quality Assurance ......................................................................... 11 1.2. The overview of Software testing fundamentals: Concepts, History, Main principles ......... 20 2. AN OVERVIEW OF SOFTWARE TESTING TECHNIQUES AND THEIR USE IN ENTERPRISES ...................................................................................................................................... 26 2.1. Testing techniques as code analysis ....................................................................................... 26 2.1.1. Static testing ...................................................................................................................... 26 2.1.2. Dynamic testing ................................................................................................................. 28 2.2. Test design based Techniques ............................................................................................... -
Ada-95: a Guide for C and C++ Programmers
Ada-95: A guide for C and C++ programmers by Simon Johnston 1995 Welcome ... to the Ada guide especially written for C and C++ programmers. Summary I have endeavered to present below a tutorial for C and C++ programmers to show them what Ada can provide and how to set about turning the knowledge and experience they have gained in C/C++ into good Ada programming. This really does expect the reader to be familiar with C/C++, although C only programmers should be able to read it OK if they skip section 3. My thanks to S. Tucker Taft for the mail that started me on this. 1 Contents 1 Ada Basics. 7 1.1 C/C++ types to Ada types. 8 1.1.1 Declaring new types and subtypes. 8 1.1.2 Simple types, Integers and Characters. 9 1.1.3 Strings. {3.6.3} ................................. 10 1.1.4 Floating {3.5.7} and Fixed {3.5.9} point. 10 1.1.5 Enumerations {3.5.1} and Ranges. 11 1.1.6 Arrays {3.6}................................... 13 1.1.7 Records {3.8}. ................................. 15 1.1.8 Access types (pointers) {3.10}.......................... 16 1.1.9 Ada advanced types and tricks. 18 1.1.10 C Unions in Ada, (food for thought). 22 1.2 C/C++ statements to Ada. 23 1.2.1 Compound Statement {5.6} ........................... 24 1.2.2 if Statement {5.3} ................................ 24 1.2.3 switch Statement {5.4} ............................. 25 1.2.4 Ada loops {5.5} ................................. 26 1.2.4.1 while Loop . -
An Introduction to Numpy and Scipy
An introduction to Numpy and Scipy Table of contents Table of contents ............................................................................................................................ 1 Overview ......................................................................................................................................... 2 Installation ...................................................................................................................................... 2 Other resources .............................................................................................................................. 2 Importing the NumPy module ........................................................................................................ 2 Arrays .............................................................................................................................................. 3 Other ways to create arrays............................................................................................................ 7 Array mathematics .......................................................................................................................... 8 Array iteration ............................................................................................................................... 10 Basic array operations .................................................................................................................. 11 Comparison operators and value testing .................................................................................... -
Worksheet 4. Matrices in Matlab
MS6021 Scientific Computation Worksheet 4 Worksheet 4. Matrices in Matlab Creating matrices in Matlab Matlab has a number of functions for generating elementary and common matri- ces. zeros Array of zeros ones Array of ones eye Identity matrix repmat Replicate and tile array blkdiag Creates block diagonal array rand Uniformly distributed randn Normally distributed random number linspace Linearly spaced vector logspace Logarithmically spaced vector meshgrid X and Y arrays for 3D plots : Regularly spaced vector : Array slicing If given a single argument they construct square matrices. octave:1> eye(4) ans = 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 If given two entries n and m they construct an n × m matrix. octave:2> rand(2,3) ans = 0.42647 0.81781 0.74878 0.69710 0.42857 0.24610 It is also possible to construct a matrix the same size as an existing matrix. octave:3> x=ones(4,5) x = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 William Lee [email protected] 1 MS6021 Scientific Computation Worksheet 4 octave:4> y=zeros(size(x)) y = 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 • Construct a 4 by 4 matrix whose elements are random numbers evenly dis- tributed between 1 and 2. • Construct a 3 by 3 matrix whose off diagonal elements are 3 and whose diagonal elements are 2. 1001 • Construct the matrix 0101 0011 The size function returns the dimensions of a matrix, while length returns the largest of the dimensions (handy for vectors). -
Slicing (Draft)
Handling Parallelism in a Concurrency Model Mischael Schill, Sebastian Nanz, and Bertrand Meyer ETH Zurich, Switzerland [email protected] Abstract. Programming models for concurrency are optimized for deal- ing with nondeterminism, for example to handle asynchronously arriving events. To shield the developer from data race errors effectively, such models may prevent shared access to data altogether. However, this re- striction also makes them unsuitable for applications that require data parallelism. We present a library-based approach for permitting parallel access to arrays while preserving the safety guarantees of the original model. When applied to SCOOP, an object-oriented concurrency model, the approach exhibits a negligible performance overhead compared to or- dinary threaded implementations of two parallel benchmark programs. 1 Introduction Writing a multithreaded program can have a variety of very different motiva- tions [1]. Oftentimes, multithreading is a functional requirement: it enables ap- plications to remain responsive to input, for example when using a graphical user interface. Furthermore, it is also an effective program structuring technique that makes it possible to handle nondeterministic events in a modular way; develop- ers take advantage of this fact when designing reactive and event-based systems. In all these cases, multithreading is said to provide concurrency. In contrast to this, the multicore revolution has accentuated the use of multithreading for im- proving performance when executing programs on a multicore machine. In this case, multithreading is said to provide parallelism. Programming models for multithreaded programming generally support ei- ther concurrency or parallelism. For example, the Actor model [2] or Simple Con- current Object-Oriented Programming (SCOOP) [3,4] are typical concurrency models: they are optimized for coordination and event handling, and provide safety guarantees such as absence of data races. -
Effective Testing for Concurrency Bugs
Effective Testing for Concurrency Bugs Thesis for obtaining the title of Doctor of Engineering Science of the Faculties of Natural Science and Technology I of Saarland University From Pedro Fonseca Saarbr¨ucken, 13. April 2015 Date of Colloquium: 24/06/2015 Dean of Faculty: Univ.-Prof. Dr. Markus Bl¨aser Chair of the Committee: Prof. Dr. Andreas Zeller First Reviewer: Prof. Dr. Rodrigo Rodrigues Second Reviewer: Prof. Dr. Peter Druschel Third Reviewer: Prof. Dr. George Candea Forth Reviewer: Dr. Bj¨orn Brandenburg Academic Assistant: Dr. Przemyslaw Grabowicz ii c 2015 Pedro Fonseca ALL RIGHTS RESERVED iii Dedicated to my parents and brother v This work was supported by the Foundation for Science and Technology of Portugal (SFRH/BD/45309/2008) and the Max Planck Society vi Abstract In the current multi-core era, concurrency bugs are a serious threat to software reliability. As hardware becomes more parallel, concurrent programming will become increasingly pervasive. However, correct concurrent programming is known to be extremely chal- lenging for developers and can easily lead to the introduction of concurrency bugs. This dissertation addresses this challenge by proposing novel techniques to help developers expose and detect concurrency bugs. We conducted a bug study to better understand the external and internal effects of real-world concurrency bugs. Our study revealed that a significant fraction of concur- rency bugs qualify as semantic or latent bugs, which are two particularly challenging classes of concurrency bugs. Based on the insights from the study, we propose a con- currency bug detector, PIKE that analyzes the behavior of program executions to infer whether concurrency bugs have been triggered during a concurrent execution. -
Declaring Matrices in Python
Declaring Matrices In Python Idiopathic Seamus regrinds her spoom so discreetly that Saul trauchled very ashamedly. Is Elvis cashesepigynous his whenpanel Husainyesternight. paper unwholesomely? Weber is slothfully terebinthine after disguisable Milo Return the array, writing about the term empty functions that can be exploring data in python matrices Returns an error message if a jitted function to declare an important advantages and! We declared within a python matrices as a line to declare an array! Transpose does this case there is. Today act this Python Array Tutorial we sure learn about arrays in Python Programming Here someone will get how Python array import module and how fly we. Matrices in Python programming Foundation Course and laid the basics to do this waterfall can initialize weights. Two-dimensional lists arrays Learn Python 3 Snakify. Asking for help, clarification, or responding to other answers. How arrogant I create 3x3 matrices Stack Overflow. What is declared a comparison operators. The same part back the array. Another Python module called array defines one-dimensional arrays so don't. By default, the elements of the bend may be leaving at all. It does not an annual step with use arrays because they there to be declared while lists don't because clothes are never of Python's syntax so lists are. How to wake a 3D NumPy array in Python Kite. Even if trigger already used Array slicing and indexing before, you may find something to evoke in this tutorial article. MATLAB Arrays as Python Variables MATLAB & Simulink. The easy way you declare array types is to subscript an elementary type according to the toil of dimensions. -
Efficient Compilation of High Level Python Numerical Programs With
Efficient Compilation of High Level Python Numerical Programs with Pythran Serge Guelton Pierrick Brunet Mehdi Amini Tel´ ecom´ Bretagne INRIA/MOAIS [email protected] [email protected] Abstract Note that due to dynamic typing, this function can take The Python language [5] has a rich ecosystem that now arrays of different shapes and types as input. provides a full toolkit to carry out scientific experiments, def r o s e n ( x ) : from core scientific routines with the Numpy package[3, 4], t 0 = 100 ∗ ( x [ 1 : ] − x [: −1] ∗∗ 2) ∗∗ 2 to scientific packages with Scipy, plotting facilities with the t 1 = (1 − x [ : − 1 ] ) ∗∗ 2 Matplotlib package, enhanced terminal and notebooks with return numpy.sum(t0 + t1) IPython. As a consequence, there has been a move from Listing 1: High-level implementation of the Rosenbrock historical languages like Fortran to Python, as showcased by function in Numpy. the success of the Scipy conference. As Python based scientific tools get widely used, the question of High performance Computing naturally arises, 1.3 Temporaries Elimination and it is the focus of many recent research. Indeed, although In Numpy, any point-to-point array operation allocates a new there is a great gain in productivity when using these tools, array that holds the computation result. This behavior is con- there is also a performance gap that needs to be filled. sistent with many Python standard module, but it is a very This extended abstract focuses on compilation techniques inefficient design choice, as it keeps on polluting the cache that are relevant for the optimization of high-level numerical with potentially large fresh storage and adds extra alloca- kernels written in Python using the Numpy package, illus- tion/deallocation operations, that have a very bad caching ef- trated on a simple kernel. -
Professional Software Testing Using Visual Studio 2019
Professional Software Testing Using Visual Studio 2019 Learn practical steps to reduce quality issues and meet release dates and budgets Shift your testing activities left to find bugs sooner Improve collaboration between the developers and testers Get hands-on experience with Visual Studio 2019, Azure DevOps Services, and common marketplace tools. Through a combination of lecture, demonstrations, and team-based exercises, learn to deliver high-quality increments of software on regular iterations using the tools found in Visual Studio, Azure DevOps Services, and the community marketplace. This three-day course provides students with practical software testing techniques and technical skills, as well as exposure to Test-Driven Development (TDD), Acceptance Test-Driven Development (ATDD), Test Impact Analysis, Continuous Integration (CI), and more. Understand and practice development testing acceptance testing, and exploratory testing. Create automated acceptance tests in Visual Studio, use SpecFlow to automate acceptance testing, and learn to use Microsoft Test Runner. Learn to plan and track work, manage test cases, and more utilizing Azure DevOps Services and associated tools. Who Should Attend This course is appropriate for all members of a software development team, especially those performing testing activities. This course also provides value for non-testers (developers, designers, managers, etc.) who want a better understanding of what agile software testing involves. This is an independent course and is neither affiliated with, nor authorized, sponsored, or approved by, Microsoft Corporation. Course Outline Agile Software Testing Exploratory Tests Overview of agile software development Introduction to exploratory tests The agile tester and agile testing practices Using the Microsoft Test & Feedback extension Different types of testing Connected mode vs. -
Professional Software Testing Using Visual Studio 2019 PTVS2019 | 3 Days
Professional Software Testing Using Visual Studio 2019 PTVS2019 | 3 Days This three-day course will introduce you to the contemporary testing principles and practices used by agile teams to deliver high-quality increments of software on regular iterations. Through a combination of lecture, demonstrations, and team-based exercises, students will experience how to do this by leveraging the tools found in Visual Studio, Azure DevOps Services, and the community marketplace. Course Objectives At course completion, attendees will have had exposure to: ✓ Agile software development and testing ✓ Acceptance Test-Driven Development (ATDD) ✓ The role of the agile tester ✓ Creating automated acceptance tests in Visual Studio ✓ Developer and tester collaboration ✓ Using SpecFlow to automate acceptance testing ✓ Agile software requirements ✓ Using Microsoft Test Runner ✓ Introduction to Azure DevOps Services ✓ Testing web and desktop applications ✓ Using Azure Boards to plan and track work ✓ Capturing screenshots and video while testing ✓ Creating, managing, and refining a product backlog ✓ Viewing and charting test run results ✓ Defining and planning for quality software ✓ Using Selenium for automated web UI testing ✓ Using Azure Test Plans for test case management ✓ Using Appium for automated desktop UI testing ✓ Creating and managing test plans ✓ Performance and load testing ✓ Organizing test cases into test suites ✓ Introduction to exploratory testing ✓ Test configurations and configuration variables ✓ Using the Microsoft Test & Feedback extension ✓ Creating and managing test cases ✓ Creating a work item during a testing session ✓ Creating parameterized test cases ✓ Exploratory testing tours ✓ Leveraging shared steps ✓ Requesting and providing stakeholder feedback ✓ Importing and exporting test artifacts ✓ Introduction to Azure Pipelines ✓ Triaging and reporting bugs ✓ Building, testing, & releasing code using Azure Pipelines ✓ Extending Azure Test Plans ✓ Hosted vs. -
Software Engineering
SOFTWARE ENGINEERING FUNDAMENTALS OF SOFTWARE TESTING Software testing is the evaluation of a system with the intention of finding an error or fault or a bug. It also checks for the functionalities of the system so that it meets the specified requirements. LEARNING OBJECTIVES • To execute a program with the intent of finding an error. • To check if the system meets the requirements and be executed successfully in the intended environment. • To check if the system is “Fit for purpose”. • To check if the system does what it is expected to do. STAGES OF TESTING Various testing is done during various phases of the development cycle such as: Module or unit testing. Integration testing, Function testing. Performance testing. Acceptance testing. Installation testing. UNIT TESTING Unit testing is the testing and validation at the unit level. Unit testing validates and tests the following: Algorithms and logic Data structures (global and local) Interfaces Independent paths Boundary conditions Error handling Formal verification. Testing the program itself by performing black box and white box testing. INTEGRATION TESTING One module can have an adverse effect on another. Sub-functions, when combined, may not produce the desired major function. Individually acceptable imprecision in calculations may be magnified to unacceptable levels. Interfacing errors not detected in unit testing may appear in this phase. Timing problems (in real-time systems) and resource contention problems are not detectable by unit testing. Top-Down Integration The main control module is used as a driver, and stubs are substituted for all modules directly subordinate to the main module. Depending on the integration approach selected (depth or breadth first), subordinate stubs are replaced by modules one at a time. -
An Analysis of the D Programming Language Sumanth Yenduri University of Mississippi- Long Beach
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by CSUSB ScholarWorks Journal of International Technology and Information Management Volume 16 | Issue 3 Article 7 2007 An Analysis of the D Programming Language Sumanth Yenduri University of Mississippi- Long Beach Louise Perkins University of Southern Mississippi- Long Beach Md. Sarder University of Southern Mississippi- Long Beach Follow this and additional works at: http://scholarworks.lib.csusb.edu/jitim Part of the Business Intelligence Commons, E-Commerce Commons, Management Information Systems Commons, Management Sciences and Quantitative Methods Commons, Operational Research Commons, and the Technology and Innovation Commons Recommended Citation Yenduri, Sumanth; Perkins, Louise; and Sarder, Md. (2007) "An Analysis of the D Programming Language," Journal of International Technology and Information Management: Vol. 16: Iss. 3, Article 7. Available at: http://scholarworks.lib.csusb.edu/jitim/vol16/iss3/7 This Article is brought to you for free and open access by CSUSB ScholarWorks. It has been accepted for inclusion in Journal of International Technology and Information Management by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [email protected]. Analysis of Programming Language D Journal of International Technology and Information Management An Analysis of the D Programming Language Sumanth Yenduri Louise Perkins Md. Sarder University of Southern Mississippi - Long Beach ABSTRACT The C language and its derivatives have been some of the dominant higher-level languages used, and the maturity has stemmed several newer languages that, while still relatively young, possess the strength of decades of trials and experimentation with programming concepts.