DOCSLIB.ORG

Advanced Python: Best Practices and Design Patterns

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Advanced Python: Best Practices and Design Patterns Course 1906 Advanced Python: Best Practices and Design Patterns by Michael Woinoski Technical Editor: Alexander Lapajne 1906/CN/C.1/911/B.1 Copyright © LEARNING TREE INTERNATIONAL, INC. All rights reserved. All trademarked product and company names are the property of their respective trademark holders. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, or translated into any language, without the prior written permission of the publisher. Copying software used in this course is prohibited without the express permission of Learning Tree International, Inc. Making unauthorized copies of such software violates federal copyright law, which includes both civil and criminal penalties. © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Access Your Do Now Course Materials 1. Log in to your My Learning Tree Account 2. Find your upcoming course, and click 3. Under Course Materials, click the links to open your course files 4. Right-click or use the icons to download and save the files to an easily accessible location © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Welcome-1 Access Your Do Now Course Materials 5. Begin highlighting and adding comments to your downloaded PDF (experiences will differ based on reader/viewer used) Take your notes with you! • Add your downloaded course materials to any cloud-based storage service (Google Drive, Dropbox, etc.) • E-mail the materials as an attachment • Save the course materials to a USB drive © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Welcome-2 Welcome! Do Now ► Meet your Instructor and Classmates • Share your: ◦ Name ◦ Background / current position ◦ Course expectations © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Welcome-3 Course Schedule ► Each day, the course will follow this schedule: • Start Class • Morning Break • Lunch • Resume Class • Afternoon Break(s) • End Time • Last-Day Course Exam ► Course logistics: • Wi-Fi information available at reception • Facilities • Local amenities • Emergency measures If we can help improve your experience, please let us know! © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Welcome-4 Introduction and Overview Course Objectives ► Employ best practices and design patterns to build Python applications that are reliable, efficient, and testable ► Exploit Python’s OOP features for stable, maintainable code ► Automate unit testing with the Unittest, Pytest, and Mock modules ► Measure and improve performance of Python applications using profiling tools, code optimization techniques, and PyPy ► Install and distribute Python programs and modules using standard Python tools, including Pip and Python Packaging Index (PyPI) OOP = object-oriented programming © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Intro-2 Course Objectives ► Create and manage concurrent threads of control ► Generate and consume REST web service requests and responses REST = representational state transfer © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Intro-3 Course Contents Introduction and Overview Chapter 1 Object-Oriented Programming in Python Chapter 2 Design Patterns in Python Chapter 3 Unit Testing and Mocking Chapter 4 Error Detection and Debugging Techniques Chapter 5 Measuring and Improving Performance Chapter 6 Design Patterns II Chapter 7 Installing and Distributing Modules Chapter 8 Concurrent Execution Chapter 9 Interfacing With REST Web Services and Clients Chapter 10 Course Summary Next Steps © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Intro-4 Course Contents Appendix A Extending Python Appendix B Advanced Python Features © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Intro-5 Goals of the Course ► Gain experience implementing design patterns in Python • Experienced programmers may use same design patterns in all languages ► You’ll be able to apply Python best practices in your code • Applying best practices results in better code ► You’ll be able to read and understand complex Python code • We’ll look at some Pythonic “magic” so you will understand how it works © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Intro-6 Expected Background ► Real-world experience in Python is expected to the level of Course 1905, Introduction to Python Training • Familiarity with the core Python language features ◦ Defining modules and packages ◦ Built-in data types, decisions (if/elif/else), looping (while, for) ◦ Defining and calling functions ◦ Exception handling: try/except/finally, raise ◦ Object-oriented programming in Python © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Intro-7 Our Classroom Environment ► Software installed on your workstations: Python 2, Python 3 Python interpreters PyCharm, IDLE Python IDEs (PyCharm for exercises) Notepad++, VS Code, Text editors Atom, vim MySQL Database for examples and exercises WSL Ubuntu Linux Windows Subsystem for Linux with Ubuntu bash line editing and command history for Windows Clink command prompt © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Intro-8 Programming Environment ► You’ll complete hands-on exercises in a Windows environment • Most examples and exercises will run unchanged on UNIX, Linux, and Mac ► Examples and exercises are compatible with Python 3 • Some Python 3 changes are not backward compatible with Python 2 • Lecture material highlights differences between Python versions • Python 3 syntax is identified this symbol: ► Exercise instructions are written for PyCharm IDE • For interactive use, PyCharm includes a Python console © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Intro-9 Hands-On Exercises ► Exercises appear throughout the course • To reinforce your learning ► All exercises have bonus sections to complete if you have more time • You won’t have time to finish them all in class! ► Hints are available for all of the coding exercises • Open a web browser and visit the course home page ► All exercises, solutions, and course examples are available for download • Visit My Learning Tree • Navigate to My Courses/Transcripts • Find Python Best Practices and Design Patterns and click View button • Under Course Notes and Exercise Manuals, click the link for Download your CD1 ► You will do the hands-on exercises on a remote Virtual Machine (VM) © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Intro-10 Accessing Your Virtual Machine (VM) Do Now ► Perform these steps to access your VM: 1. Open Chrome and navigate to https://www.learningtree.com 2. Log in to My Learning Tree ◦ Your instructor will give you a temporary password if you need it 3. Click Course History > Course Enrollments > Advanced Python Best Practices and Design Patterns > Details 4. Click Access My Virtual Machine 5. Click the RDP button (above the thumbnail of the desktop) 6. Click Download RDP 7. Click the downloaded file in the Chrome status bar 8. Click Connect 9. Click Yes in the warning dialog about the remote computer’s identity 10.Enter your credentials ◦ Username: user ◦ Password: pw 11.Right-click the desktop > Screen Resolution and select the resolution of your monitor © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Intro-11 Course Notes ► The Course Notes are organized to facilitate learning • Not intended to be used as a primary reference ► The Course Notes provide the big picture, then slowly fill in more detail • Suggested reference: standard Python documentation ◦ Available as Windows help files or online ◦ Online: https://docs.python.org/3/ • For more information, see Supplemental Course Materials (SCM) on My Learning Tree © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. Intro-12 Chapter 1 Object-Oriented Programming in Python Objectives ► Define Python classes and create objects ► Extend classes to define subclasses using inheritance © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. 1-2 Contents Python Feature Review ► OOP in Python ► Type Hinting ► Hands-On Exercise 1.1 © Learning Tree International, Inc. All rights reserved. Not to be reproduced without prior written consent. 1-3 Review: List Data Type ► list: Mutable ordered sequence of items • Create a list with [] cities = ['London', 'Ottawa', 'Stockholm'] populations = [8.6, 0.9, 0.9] # populations in millions ► To select a list item, use a numeric index print('The value of', cities[0], ' is', populations[0]) ► List operations • Select item by index: populations[1] • Select range of items (slice): cities[0:2] Selects items start to (end - 1) ◦ Selects ['London', 'Ottawa'] • Append item: cities.append('Washington DC') • Process all
Load more

Recommended publications
  • Python on Gpus (Work in Progress!)
    Python on GPUs (work in progress!) Laurie Stephey GPUs for Science Day, July 3, 2019 Rollin Thomas, NERSC Lawrence Berkeley National Laboratory Python is friendly and popular Screenshots from: https://www.tiobe.com/tiobe-index/ So you want to run Python on a GPU? You have some Python code you like. Can you just run it on a GPU? import numpy as np from scipy import special import gpu ? Unfortunately no. What are your options? Right now, there is no “right” answer ● CuPy ● Numba ● pyCUDA (https://mathema.tician.de/software/pycuda/) ● pyOpenCL (https://mathema.tician.de/software/pyopencl/) ● Rewrite kernels in C, Fortran, CUDA... DESI: Our case study Now Perlmutter 2020 Goal: High quality output spectra Spectral Extraction CuPy (https://cupy.chainer.org/) ● Developed by Chainer, supported in RAPIDS ● Meant to be a drop-in replacement for NumPy ● Some, but not all, NumPy coverage import numpy as np import cupy as cp cpu_ans = np.abs(data) #same thing on gpu gpu_data = cp.asarray(data) gpu_temp = cp.abs(gpu_data) gpu_ans = cp.asnumpy(gpu_temp) Screenshot from: https://docs-cupy.chainer.org/en/stable/reference/comparison.html eigh in CuPy ● Important function for DESI ● Compared CuPy eigh on Cori Volta GPU to Cori Haswell and Cori KNL ● Tried “divide-and-conquer” approach on both CPU and GPU (1, 2, 5, 10 divisions) ● Volta wins only at very large matrix sizes ● Major pro: eigh really easy to use in CuPy! legval in CuPy ● Easy to convert from NumPy arrays to CuPy arrays ● This function is ~150x slower than the cpu version! ● This implies there
    [Show full text]
  • Introduction Shrinkage Factor Reference
    Comparison study for implementation efficiency of CUDA GPU parallel computation with the fast iterative shrinkage-thresholding algorithm Younsang Cho, Donghyeon Yu Department of Statistics, Inha university 4. TensorFlow functions in Python (TF-F) Introduction There are some functions executed on GPU in TensorFlow. So, we implemented our algorithm • Parallel computation using graphics processing units (GPUs) gets much attention and is just using that functions. efficient for single-instruction multiple-data (SIMD) processing. 5. Neural network with TensorFlow in Python (TF-NN) • Theoretical computation capacity of the GPU device has been growing fast and is much higher Neural network model is flexible, and the LASSO problem can be represented as a simple than that of the CPU nowadays (Figure 1). neural network with an ℓ1-regularized loss function • There are several platforms for conducting parallel computation on GPUs using compute 6. Using dynamic link library in Python (P-DLL) unified device architecture (CUDA) developed by NVIDIA. (Python, PyCUDA, Tensorflow, etc. ) As mentioned before, we can load DLL files, which are written in CUDA C, using "ctypes.CDLL" • However, it is unclear what platform is the most efficient for CUDA. that is a built-in function in Python. 7. Using dynamic link library in R (R-DLL) We can also load DLL files, which are written in CUDA C, using "dyn.load" in R. FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) We consider FISTA (Beck and Teboulle, 2009) with backtracking as the following: " Step 0. Take �! > 0, some � > 1, and �! ∈ ℝ . Set �# = �!, �# = 1. %! Step k. � ≥ 1 Find the smallest nonnegative integers �$ such that with �g = � �$&# � �(' �$ ≤ �(' �(' �$ , �$ .
    [Show full text]
  • Prototyping and Developing GPU-Accelerated Solutions with Python and CUDA Luciano Martins and Robert Sohigian, 2018-11-22 Introduction to Python
    Prototyping and Developing GPU-Accelerated Solutions with Python and CUDA Luciano Martins and Robert Sohigian, 2018-11-22 Introduction to Python GPU-Accelerated Computing NVIDIA® CUDA® technology Why Use Python with GPUs? Agenda Methods: PyCUDA, Numba, CuPy, and scikit-cuda Summary Q&A 2 Introduction to Python Released by Guido van Rossum in 1991 The Zen of Python: Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Interpreted language (CPython, Jython, ...) Dynamically typed; based on objects 3 Introduction to Python Small core structure: ~30 keywords ~ 80 built-in functions Indentation is a pretty serious thing Dynamically typed; based on objects Binds to many different languages Supports GPU acceleration via modules 4 Introduction to Python 5 Introduction to Python 6 Introduction to Python 7 GPU-Accelerated Computing “[T]the use of a graphics processing unit (GPU) together with a CPU to accelerate deep learning, analytics, and engineering applications” (NVIDIA) Most common GPU-accelerated operations: Large vector/matrix operations (Basic Linear Algebra Subprograms - BLAS) Speech recognition Computer vision 8 GPU-Accelerated Computing Important concepts for GPU-accelerated computing: Host ― the machine running the workload (CPU) Device ― the GPUs inside of a host Kernel ― the code part that runs on the GPU SIMT ― Single Instruction Multiple Threads 9 GPU-Accelerated Computing 10 GPU-Accelerated Computing 11 CUDA Parallel computing
    [Show full text]
  • Tangent: Automatic Differentiation Using Source-Code Transformation for Dynamically Typed Array Programming
    Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming Bart van Merriënboer Dan Moldovan Alexander B Wiltschko MILA, Google Brain Google Brain Google Brain [email protected] [email protected] [email protected] Abstract The need to efficiently calculate first- and higher-order derivatives of increasingly complex models expressed in Python has stressed or exceeded the capabilities of available tools. In this work, we explore techniques from the field of automatic differentiation (AD) that can give researchers expressive power, performance and strong usability. These include source-code transformation (SCT), flexible gradient surgery, efficient in-place array operations, and higher-order derivatives. We implement and demonstrate these ideas in the Tangent software library for Python, the first AD framework for a dynamic language that uses SCT. 1 Introduction Many applications in machine learning rely on gradient-based optimization, or at least the efficient calculation of derivatives of models expressed as computer programs. Researchers have a wide variety of tools from which they can choose, particularly if they are using the Python language [21, 16, 24, 2, 1]. These tools can generally be characterized as trading off research or production use cases, and can be divided along these lines by whether they implement automatic differentiation using operator overloading (OO) or SCT. SCT affords more opportunities for whole-program optimization, while OO makes it easier to support convenient syntax in Python, like data-dependent control flow, or advanced features such as custom partial derivatives. We show here that it is possible to offer the programming flexibility usually thought to be exclusive to OO-based tools in an SCT framework.
    [Show full text]
  • From Zero to 1000 Tests in 6 Months
    From Zero to 1000 tests in 6 months Or how not to lose your mind with 2 week iterations Name is Max Vasilyev Senior Developer and QA Manager at Solutions Aberdeen http://tech.trailmax.info @trailmax Business Does Not Care • Business does not care about tests. • Business does not care about internal software quality. • Business does not care about architecture. • Some businesses don’t care so much, they even don’t care about money. Don’t Tell The Business Just do it! Just write your tests, ask no one. Honestly, tomorrow in the office just create new project, add NUnit package and write a test. That’ll take you 10 minutes. Simple? Writing a test is simple. Writing a good test is hard. Main questions are: – What do you test? – Why do you test? – How do you test? Our Journey: Stone Age Started with Selenium browser tests: • Recording tool is OK to get started • Boss loved it! • Things fly about on the screen - very dramatic But: • High maintenance effort • Problematic to check business logic Our Journey: Iron Age After initial Selenium fever, moved on to integration tests: • Hook database into tests and part-test database. But: • Very difficult to set up (data + infrastructure) • Problematic to test logic Our Journey: Our Days • Now no Selenium tests • A handful of integration tests • Most of the tests are unit-ish* tests • 150K lines of code in the project • Around 1200 tests with 30% coverage** • Tests are run in build server * Discuss Unit vs Non-Unit tests later ** Roughly 1 line of test code covers 2 lines of production code Testing Triangle GUI Tests GUI Tests Integration Tests Integration Tests Unit Tests Unit Tests Our Journey: 2 Week Iterations? The team realised tests are not optional after first 2-week iteration: • There simply was no time to manually test everything at the end of iteration.
    [Show full text]
  • Designpatternsphp Documentation Release 1.0
    DesignPatternsPHP Documentation Release 1.0 Dominik Liebler and contributors Jul 18, 2021 Contents 1 Patterns 3 1.1 Creational................................................3 1.1.1 Abstract Factory........................................3 1.1.2 Builder.............................................8 1.1.3 Factory Method......................................... 13 1.1.4 Pool............................................... 18 1.1.5 Prototype............................................ 21 1.1.6 Simple Factory......................................... 24 1.1.7 Singleton............................................ 26 1.1.8 Static Factory.......................................... 28 1.2 Structural................................................. 30 1.2.1 Adapter / Wrapper....................................... 31 1.2.2 Bridge.............................................. 35 1.2.3 Composite............................................ 39 1.2.4 Data Mapper.......................................... 42 1.2.5 Decorator............................................ 46 1.2.6 Dependency Injection...................................... 50 1.2.7 Facade.............................................. 53 1.2.8 Fluent Interface......................................... 56 1.2.9 Flyweight............................................ 59 1.2.10 Proxy.............................................. 62 1.2.11 Registry............................................. 66 1.3 Behavioral................................................ 69 1.3.1 Chain Of Responsibilities...................................
    [Show full text]
  • Python Guide Documentation 0.0.1
    Python Guide Documentation 0.0.1 Kenneth Reitz 2015 11 07 Contents 1 3 1.1......................................................3 1.2 Python..................................................5 1.3 Mac OS XPython.............................................5 1.4 WindowsPython.............................................6 1.5 LinuxPython...............................................8 2 9 2.1......................................................9 2.2...................................................... 15 2.3...................................................... 24 2.4...................................................... 25 2.5...................................................... 27 2.6 Logging.................................................. 31 2.7...................................................... 34 2.8...................................................... 37 3 / 39 3.1...................................................... 39 3.2 Web................................................... 40 3.3 HTML.................................................. 47 3.4...................................................... 48 3.5 GUI.................................................... 49 3.6...................................................... 51 3.7...................................................... 52 3.8...................................................... 53 3.9...................................................... 58 3.10...................................................... 59 3.11...................................................... 62
    [Show full text]
  • Stream-Based Verification with Junit
    Stream-Based Verification with JUnit Strombasierte Verifikation mit JUnit Masterarbeit Im Rahmen des Studiengangs Vorgelegt von Ausgegeben und betreut von Informatik Denis-Michael Lux Prof. Dr. Martin Leucker der Universität zu Lübeck mit Unterstützung von Malte Schmitz, M.Sc. Dipl.-Inf. Daniel Thoma Lübeck, den 14. Juni 2019 Selbstständigkeitserklärung Der Verfasser versichert an Eides statt, dass er die vorliegende Arbeit selbständig, ohne fremde Hilfe und ohne Benutzung anderer als der angegebenen Hilfsmittel angefertigt hat. Die aus fremden Quellen (einschließlich elektronischer Quellen) direkt oder indirekt über- nommenen Gedanken sind ausnahmslos als solche kenntlich gemacht. Die Arbeit ist in gle- icher oder ähnlicher Form oder auszugsweise im Rahmen einer anderen Prüfung noch nicht vorgelegt worden. Ort/Datum Unterschrift des Verfassers Contents Introduction 1 1 Unit Testing and Mocking in JUnit 3 1.1 Unit Testing ................................... 4 1.2 The JUnit Testing Framework ......................... 4 1.3 Assertions .................................... 8 1.4 Mocks and Stubs ................................ 10 2 Stream Runtime Verification 13 2.1 Runtime Verfication .............................. 13 2.2 Stream Processing ............................... 14 2.3 Temporal Stream-based Specification Language (TeSSLa) .......... 20 3 TeSSLa-Based Monitoring and Mocking in JUnit 25 3.1 Application Code Instrumentation ...................... 25 3.2 Monitors for Test Cases and Test Suites .................... 28 3.3 Class and Interface
    [Show full text]
  • Hyperlearn Documentation Release 1
    HyperLearn Documentation Release 1 Daniel Han-Chen Jun 19, 2020 Contents 1 Example code 3 1.1 hyperlearn................................................3 1.1.1 hyperlearn package.......................................3 1.1.1.1 Submodules......................................3 1.1.1.2 hyperlearn.base module................................3 1.1.1.3 hyperlearn.linalg module...............................7 1.1.1.4 hyperlearn.utils module................................ 10 1.1.1.5 hyperlearn.random module.............................. 11 1.1.1.6 hyperlearn.exceptions module............................. 11 1.1.1.7 hyperlearn.multiprocessing module.......................... 11 1.1.1.8 hyperlearn.numba module............................... 11 1.1.1.9 hyperlearn.solvers module............................... 12 1.1.1.10 hyperlearn.stats module................................ 14 1.1.1.11 hyperlearn.big_data.base module........................... 15 1.1.1.12 hyperlearn.big_data.incremental module....................... 15 1.1.1.13 hyperlearn.big_data.lsmr module........................... 15 1.1.1.14 hyperlearn.big_data.randomized module....................... 16 1.1.1.15 hyperlearn.big_data.truncated module........................ 17 1.1.1.16 hyperlearn.decomposition.base module........................ 18 1.1.1.17 hyperlearn.decomposition.NMF module....................... 18 1.1.1.18 hyperlearn.decomposition.PCA module........................ 18 1.1.1.19 hyperlearn.decomposition.PCA module........................ 18 1.1.1.20 hyperlearn.discriminant_analysis.base
    [Show full text]
  • Zmcintegral: a Package for Multi-Dimensional Monte Carlo Integration on Multi-Gpus
    ZMCintegral: a Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs Hong-Zhong Wua,∗, Jun-Jie Zhanga,∗, Long-Gang Pangb,c, Qun Wanga aDepartment of Modern Physics, University of Science and Technology of China bPhysics Department, University of California, Berkeley, CA 94720, USA and cNuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA Abstract We have developed a Python package ZMCintegral for multi-dimensional Monte Carlo integration on multiple Graphics Processing Units(GPUs). The package employs a stratified sampling and heuristic tree search algorithm. We have built three versions of this package: one with Tensorflow and other two with Numba, and both support general user defined functions with a user- friendly interface. We have demonstrated that Tensorflow and Numba help inexperienced scientific researchers to parallelize their programs on multiple GPUs with little work. The precision and speed of our package is compared with that of VEGAS for two typical integrands, a 6-dimensional oscillating function and a 9-dimensional Gaussian function. The results show that the speed of ZMCintegral is comparable to that of the VEGAS with a given precision. For heavy calculations, the algorithm can be scaled on distributed clusters of GPUs. Keywords: Monte Carlo integration; Stratified sampling; Heuristic tree search; Tensorflow; Numba; Ray. PROGRAM SUMMARY Manuscript Title: ZMCintegral: a Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs arXiv:1902.07916v2 [physics.comp-ph] 2 Oct 2019 Authors: Hong-Zhong Wu; Jun-Jie Zhang; Long-Gang Pang; Qun Wang Program Title: ZMCintegral Journal Reference: ∗Both authors contributed equally to this manuscript. Preprint submitted to Computer Physics Communications October 3, 2019 Catalogue identifier: Licensing provisions: Apache License Version, 2.0(Apache-2.0) Programming language: Python Operating system: Linux Keywords: Monte Carlo integration; Stratified sampling; Heuristic tree search; Tensorflow; Numba; Ray.
    [Show full text]
  • Autonomic Test Case Generation of Failing Code Using AOP By
    Autonomic Test Case Generation of Failing Code Using AOP by Giovanni Murguia B.Sc., Instituto Tecnol´ogicoy de Estudios Superiores de Monterrey, Mexico 2008 A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Department of Computer Science c Giovanni Murguia, 2020 University of Victoria All rights reserved. This thesis may not be reproduced in whole or in part, by photocopying or other means, without the permission of the author. ii Autonomic Test Case Generation of Failing Code Using AOP by Giovanni Murguia B.Sc., Instituto Tecnol´ogicoy de Estudios Superiores de Monterrey, Mexico 2008 Supervisory Committee Dr. Hausi A. M¨uller,Supervisor (Department of Computer Science) Dr. Alex I. Thomo, Departamental Member (Department of Computer Science) iii Supervisory Committee Dr. Hausi A. M¨uller,Supervisor (Department of Computer Science) Dr. Alex I. Thomo, Departamental Member (Department of Computer Science) ABSTRACT As software systems have grown in size and complexity, the costs of maintaining such systems increases steadily. In the early 2000's, IBM launched the autonomic com- puting initiative to mitigate this problem by injecting feedback control mechanisms into software systems to enable them to observe their health and self-heal without human intervention and thereby cope with certain changes in their requirements and environments. Self-healing is one of several fundamental challenges addressed and includes software systems that are able to recover from failure conditions. There has been considerable research on software architectures with feedback loops that allow a multi-component system to adjust certain parameters automatically in response to changes in its environment.
    [Show full text]
  • Test-‐Driven Development Step Patterns for Designing Objects
    Test-Driven Development Step Patterns For Designing Objects Dependencies EDUARDO GUERRA, National Institute for Space Research, Brazil JOSEPH YODER, Refactory Inc., USA MAURÍCIO FINAVARO ANICHE, University of São Paulo, Brazil MARCO AURÉLIO GEROSA, University of São Paulo, Brazil Test-driven development (TDD) is a development technique often used to design classes in a software system by creating tests before their actual code. The dependency management and class APIs decisions, that emerge during the practice of TDD, does not "just happen": the way that the tests are created should be used in this process to make decisions and drive the design in the desired direction. This paper introduces four patterns that document the kinds of TDD cycles that can be performed to guide the design in the desired direction. These patterns are part of a pattern language that intends to present recurrent solutions that are used in a TDD process. Categories and Subject Descriptors: D.1.5 [Programming Techniques]: Object-oriented Programming; D.2.11 [Software Architectures]: Patterns General Terms: Test driven development Additional Key Words and Phrases: TDD, softWare design, patterns ACM Reference Format: Guerra, E., Yoder, J., Aniche, M. and Gerosa, M.. 2013. Test-Driven Development Step Patterns For Designing Objects Dependencies. Proceedings of the 20th Conference on Pattern Languages of Programs (PLoP). October 2013, 15 pages. 1. INTRODUCTION Test-driven development (TDD) is a technique in Which the tests are Written before the production code (Beck 2002). By using it, the development occurs in cycles, comprised of the creation of an automated test, an update on the developed softWare to make the test pass, and a code refactoring to improve the solution.
    [Show full text]