Limited to a strict interpretation of its definition, open source consists of a set of rules which apply to a piece of software and which specify how the software and derivatives of it may be used. However, it is widely seen as much more than a simple licensing agreement, it is a ´´philoshophy´´, a ´´production model´´, a ´´way of organizing projects´´, or even ´´a new innovation model´´. But how are open source projects organized and how is work coordinated and distributed between its developers? This work contributes by examining actual source code changes, comparing 29 projects. Which developers collaborate in the same files and wich work exclusively in their own domain? Looking for patterns across projects, this work attempts to identify coordination styles in open source projects.
Today, reliable software systems are the basis of any business or company. The continuous further development of those systems is the central component in software evolution. It requires a huge amount of time- man power- as well as financial resources. The challenges are size, seniority and heterogeneity of those software systems. Christian Wagner addresses software evolution: the inherent problems and uncertainties in the process. He presents a model-driven method which leads to a synchronization between source code and design. As a result the model layer will be the central part in further evolution and source code becomes a by-product. For the first time a model-driven procedure for maintenance and migration of software systems is described. The procedure is composed of a model-driven reengineering and a model-driven migration phase. The application and effectiveness of the procedure are confirmed with a reference implementation applied to four exemplary systems.
Familiarize yourself with the basics of Python for engineering and scientific computations using this concise, practical tutorial that is focused on writing code to learn concepts. Introduction to Python is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation. In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you´ll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts. What You´ll Learn Understand the fundamentals of the Python programming language Apply Python to numerical computational programming projects in engineering and science Discover the Pythonic way of life Apply data types, operators, and arrays Carry out plotting for visualization Work with functions and loops Who This Book Is For Engineers, scientists, researchers, and students who are new to Python. Some prior programming experience would be helpful but not required.
Since the 70s, the C preprocessor is still widely used in practice in a number of projects to tailor systems to different platforms and application scenarios. In academia, researchers have criticized its lack of separation of concerns, its proneness to introduce subtle errors, and its obfuscation of the source code. To better understand the problems of using the C preprocessor, we conducted 40 interviews and a survey among 202 developers. We found that developers deal with three common problems in practice: configuration-related bugs, combinatorial testing, and code comprehension. To better deal with these problems, this book presents strategies to detect bugs and bad smells in preprocessor-based systems based on variability-aware analysis and sampling. This work presents useful findings for C developers during their development tasks, contributing to minimize the chances of introducing configuration-related bugs and bad smells, improve code comprehension, and guide developers to perform combinatorial testing.
Social machines are a type of network connected by interactive digital devices made possible by the ubiquitous adoption of technologies such as the Internet, the smartphone, social media and the read/write World Wide Web, connecting people at scale to document situations, cooperate on tasks, exchange information, or even simply to play. Existing social processes may be scaled up, and new social processes enabled, to solve problems, augment reality, create new sources of value, and disrupt existing practice. This book considers what talents one would need to understand or build a social machine, describes the state of the art, and speculates on the future, from the perspective of the EPSRC project SOCIAM - The Theory and Practice of Social Machines. The aim is to develop a set of tools and techniques for investigating, constructing and facilitating social machines, to enable us to narrow down pragmatically what is becoming a wide space, by asking ´when will it be valuable to use these methods on a sociotechnical system?´ The systems for which the use of these methods adds value are social machines in which there is rich person-to-person communication, and where a large proportion of the machine´s behaviour is constituted by human interaction.
Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using this book, you´ll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment alternatives), and even games. Up until very recently, Python was mostly regarded as just a web scripting language. Well, computational scientists and engineers have recently discovered the flexibility and power of Python to do more. Big data analytics and cloud computing programmers are seeing Python´s immense use. Financial engineers are also now employing Python in their work. Python seems to be evolving as a language that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical computations.
Quickly learn how to automate unit testing of Python 3 code with Python 3 automation libraries, such as doctest, unittest, nose, nose2, and pytest. This book explores the important concepts in software testing and their implementation in Python 3 and shows you how to automate, organize, and execute unit tests for this language. This knowledge is often acquired by reading source code, manuals, and posting questions on community forums, which tends to be a slow and painful process. Python Unit Test Automation will allow you to quickly ramp up your understanding of unit test libraries for Python 3 through the practical use of code examples and exercises. All of which makes this book a great resource for software developers and testers who want to get started with unit test automation in Python 3 and compare the differences with Python 2. This short work is your must-have quick start guide to mastering the essential concepts of software testing in Python. What You´ll Learn: Essential concepts in software testing Various test automation libraries for Python, such as doctest, unittest, nose, nose2, and pytest Test-driven development and best practices for test automation in Python Code examples and exercises Who This Book Is For: Python developers, software testers, open source enthusiasts, and contributors to the Python community
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
Learn Intel 64 assembly language and architecture, become proficient in C, and understand how the programs are compiled and executed down to machine instructions, enabling you to write robust, high-performance code. Low-Level Programming explains Intel 64 architecture as the result of von Neumann architecture evolution. The book teaches the latest version of the C language (C11) and assembly language from scratch. It covers the entire path from source code to program execution, including generation of ELF object files, and static and dynamic linking. Code examples and exercises are included along with the best code practices. Optimization capabilities and limits of modern compilers are examined, enabling you to balance between program readability and performance. The use of various performance-gain techniques is demonstrated, such as SSE instructions and pre-fetching. Relevant Computer Science topics such as models of computation and formal grammars are addressed, and their practical value explained. What You´ll Learn Low-Level Programming teaches programmers to: Freely write in assembly language Understand the programming model of Intel 64 Write maintainable and robust code in C11 Follow the compilation process and decipher assembly listings Debug errors in compiled assembly code Use appropriate models of computation to greatly reduce program complexity Write performance-critical code Comprehend the impact of a weak memory model in multi-threaded applications Who This Book Is For Intermediate to advanced programmers and programming students
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an ´´Introduction to Data Science´´ course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains ´´War Stories,´´ offering perspectives on how data science applies in the real world Includes ´´Homework Problems,´´ providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides ´´Take-Home Lessons,´´ emphasizing the big-picture concepts to learn from each chapter Recommends exciting ´´Kaggle Challenges´´ from the online platform Kaggle Highlights ´´False Starts,´´ revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show ´´The Quant Shop´´ (www.quant-shop.com)