Scala测试框架详解

Test,scala
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"Testing in Scala.pdf 是一本由 Daniel Hinojosa 撰写的关于 Scala 测试框架的书籍，ISBN 号为 978-1-449-31511-5。该书由 O'Reilly Media 出版，并提供了在线版本。本书主要探讨如何在 Scala 开发环境中进行有效的测试工作。" 在 Scala 这种函数式编程语言中，测试扮演着至关重要的角色，因为它有助于确保代码的质量和可维护性。Scala 支持多种测试框架，这些框架使得测试过程更加高效和灵活。以下是 Scala 测试框架的一些关键知识点： 1. **ScalaTest**：ScalaTest 是最广泛使用的 Scala 测试框架之一，它提供了丰富的匹配器和断言，支持行为驱动开发（BDD）和测试驱动开发（TDD）。例如，可以使用 `should`、`must` 和 `can` 方法来编写易于理解的测试用例。 2. **ScalaCheck**：ScalaCheck 是一个自动化测试库，它基于属性检验。通过生成随机数据来测试代码的边界条件和异常情况，从而增强测试的覆盖率。ScalaCheck 可以与 ScalaTest 结合使用，提供更强大的测试能力。 3. **specs2**：另一个流行的 Scala 测试框架，specs2 提供了类似于 ScalaTest 的 BDD 风格，但同时支持契约测试和端到端测试。它允许开发者定义规范，然后验证代码是否满足这些规范。 4. **Mocking**：在 Scala 中，可以使用如 Mockito 或 Scalatest 自带的模拟功能来创建和测试对象的交互。这使得在测试时可以隔离依赖，只关注正在测试的代码部分。 5. **TestNG**：虽然 TestNG 主要是为 Java 设计的，但它也可以在 Scala 中使用。TestNG 提供了并发测试、测试套件管理和灵活的报告等特性，适合大型项目。 6. **Akka 测试**：当与 Akka 框架一起工作时，Akka 提供了自己的测试工具包，如 Akka TestKit，用于测试 Actor 系统。它使得编写和执行并发测试变得更加简单。 7. **持续集成(CI)**：在实际项目中，通常会结合 Jenkins、Travis CI 或 CircleCI 等持续集成工具，自动化执行测试，确保每次代码提交后都能快速发现问题。 测试在 Scala 开发中的重要性在于它能够帮助开发者尽早发现和修复错误，提高软件的可靠性。通过深入理解和应用这些测试框架，开发者可以构建出更加健壮、可信赖的系统。而《Testing in Scala》这本书将为读者提供这些工具的详细介绍和实践指导。

Testing in Scala-带书签目录超清文字版.pdf

2018-11-11 上传
Testing in Scala-带书签目录超清文字版.pdf 这个是带完整目录书签的文字版本，文本内容可以复制的哦

Visual Knowledge Discovery and Machine Learning-Springer(2018).pdf

2018-01-23 上传
Emergence of Data Science placed knowledge discovery, machine learning, and data mining in multidimensional data, into the forefront of a wide range of current research, and application activities in computer science, and many domains far beyond it. Discovering patterns, in multidimensional data, using a combination of visual and analytical machine learning means are an attractive visual analytics opportu- nity. It allows the injection of the unique human perceptual and cognitive abilities, directly into the process of discovering multidimensional patterns. While this opportunity exists, the long-standing problem is that we cannot see the n-D data with a naked eye. Our cognitive and perceptual abilities are perfected only in the 3-D physical world. We need enhanced visualization tools (“n-D glasses”) to represent the n-D data in 2-D completely, without loss of information, which is important for knowledge discovery. While multiple visualization methods for the n-D data have been developed and successfully used for many tasks, many of them are non-reversible and lossy. Such methods do not represent the n-D data fully and do not allow the restoration of the n-D data completely from their 2-D represen- tation. Respectively, our abilities to discover the n-D data patterns, from such incomplete 2-D representations, are limited and potentially erroneous. The number of available approaches, to overcome these limitations, is quite limited itself. The Parallel Coordinates and the Radial/Star Coordinates, today, are the most powerful reversible and lossless n-D data visualization methods, while suffer from occlusion. There is a need to extend the class of reversible and lossless n-D data visual representations, for the knowledge discovery in the n-D data. A new class of such representations, called the General Line Coordinate (GLC) and several of their specifications, are the focus of this book. This book describes the GLCs, and their advantages, which include analyzing the data of the Challenger disaster, World hunger, semantic shift in humorous texts, image processing, medical computer-aided diag- nostics, stock market, and the currency exchange rate predictions. Reversible methods for visualizing the n-D data have the advantages as cognitive enhancers, of the human cognitive abilities, to discover the n-D data patterns. This book reviews the state of the vii viii Preface art in this area, outlines the challenges, and describes the solutions in the framework of the General Line Coordinates. This book expands the methods of the visual analytics for the knowledge dis- covery, by presenting the visual and hybrid methods, which combine the analytical machine learning and the visual means. New approaches are explored, from both the theoretical and the experimental viewpoints, using the modeled and real data. The inspiration, for a new large class of coordinates, is twofold. The first one is the marvelous success of the Parallel Coordinates, pioneered by Alfred Inselberg. The second inspiration is the absence of a “silver bullet” visualization, which is perfect for the pattern discovery, in the all possible n-D datasets. Multiple GLCs can serve as a collective “silver bullet.” This multiplicity of GLCs increases the chances that the humans will reveal the hidden n-D patterns in these visualizations. The topic of this book is related to the prospects of both the super-intelligent machines and the super-intelligent humans, which can far surpass the current human intelligence, significantly lifting the human cognitive limitations. This book is about a technical way for reaching some of the aspects of super-intelligence, which are beyond the current human cognitive abilities. It is to overcome the inabilities to analyze a large amount of abstract, numeric, and high-dimensional data; and to find the complex patterns, in these data, with a naked eye, supported by the analytical means of machine learning. The new algorithms are presented for the reversible GLC visual representations of high-dimensional data and knowledge discovery. The advantages of GLCs are shown, both mathematically and using the different datasets. These advantages form a basis, for the future studies, in this super-intelligence area. This book is organized as follows. Chapter 1 presents the goal, motivation, and the approach. Chapter 2 introduces the concept of the General Line Coordinates, which is illustrated with multiple examples. Chapter 3 provides the rigorous mathematical definitions of the GLC concepts along with the mathematical state- ments of their properties. A reader, interested only in the applied aspects of GLC, can skip this chapter. A reader, interested in implementing GLC algorithms, may find Chap. 3 useful for this. Chapter 4 describes the methods of the simplification of visual patterns in GLCs for the better human perception. Chapter 5 presents several GLC case studies, on the real data, which show the GLC capabilities. Chapter 6 presents the results of the experiments on discovering the visual features in the GLCs by multiple participants, with the analysis of the human shape perception capabilities with over hundred dimensions, in these experiments. Chapter 7 presents the linear GLCs combined with machine learning, including hybrid, automatic, interactive, and collaborative versions of linear GLC, with the data classification applications from medicine to finance and image pro- cessing. Chapter 8 demonstrates the hybrid, visual, and analytical knowledge dis- covery and the machine learning approach for the investment strategy with GLCs. Chapter 9 presents a hybrid, visual, and analytical machine learning approach in text mining, for discovering the incongruity in humor modeling. Chapter 10 describes the capabilities of the GLC visual means to enhance evaluation of accuracy and errors of machine learning algorithms. Chapter 11 shows an approach, Preface ix to how the GLC visualization benefits the exploration of the multidimensional Pareto front, in multi-objective optimization tasks. Chapter 12 outlines the vision of a virtual data scientist and the super-intelligence with visual means. Chapter 13 concludes this book with a comparison and the fusion of methods and the dis- cussion of the future research. The final note is on the topics, which are outside of this book. These topics are “goal-free” visualizations that are not related to the specific knowledge discovery tasks of supervised and unsupervised learning, and the Pareto optimization in the n-D data. The author’s Web site of this book is located at http://www.cwu.edu/*borisk/visualKD, where additional information and updates can be found. Ellensburg, USA Boris Kovalerchuk

testing in scala

2014-09-02 上传
Testing in Scala starts with an introduction of the Scala programming language, explains why project infrastructure is critical, and provides compelling reasons to use Scala testing frameworks to not only test Scala code, but Java code too. This book introduces and explains the Simple Build Tool, the Scala answer to Ant, Maven, Gradle, and Buildr. It then explains in detail all the best tools for testing Scala and Java code today--ScalaTest, Specs2, ScalaCheck, and Borachio. Testing in Scala also gives insight on how to integrate coverage tools, continuous integration, web acceptance frameworks, and Java testing frameworks that you already know and love.

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