Java 是一门编程语言,拥有跨平台、面向对象、泛型编程等特性。

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从一开始,Java就席卷了编程世界。它的特性和功能为开发人员提供了编写健壮的跨平台应用程序所需的工具。Java基础向您介绍这些工具和功能,它们将使您能够创建Java程序。

本书首先介绍了该语言,它的哲学,以及随着时间的演变,直到最新的版本。您将了解javac/java工具是如何工作的,以及java包是什么——java程序通常是如何组织的。一旦您熟悉了这一点,您将了解该语言的高级概念,如控制流关键字。

您将探索面向对象编程以及它在使Java成为现在的样子中所扮演的角色。在结束语中,您将掌握类、类型转换和接口,并理解数据结构、数组、字符串、异常处理和创建泛型的使用。

在本书结束时,您将学会编写程序,自动化任务,学习算法和数据结构的高级课程,或探索更高级的Java课程。

你会学到什么 在代码中使用数据类型、数据结构和控制流创建和运行Java程序 在创建对象时实现最佳实践 使用构造函数和继承 了解先进的数据结构,以组织和存储数据 在编译期间为更强的检查类型使用泛型 学习在代码中处理异常

这本书是给谁的 Java基础是为那些熟悉一些编程语言并想要快速介绍Java最重要原理的技术爱好者设计的。

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Testing is widely recognized as an important stage of the software development lifecycle. Effective software testing can provide benefits such as documentation, bug finding, and preventing regressions. In particular, unit tests document a unit's \textit{intended} functionality. A \textit{test oracle}, typically expressed as an condition, documents the intended behavior of the unit under a given test prefix. Synthesizing a functional test oracle is a challenging problem, as it has to capture the intended functionality and not the implemented functionality. In this paper, we propose \toolName{} (\underline{N}eural \underline{U}nit \underline{T}est \underline{S}uggestions), a unified transformer-based neural approach to infer both exceptional and assertion test oracles based on the context of the focal method. Our approach can handle units with ambiguous or missing documentations, and even units with a missing implementation. We evaluate our approach on both oracle inference accuracy and functional bug-finding. Our technique improves accuracy by 33\% over existing oracle inference approaches, achieving 96\% overall accuracy on a held out test dataset. Furthermore, we show that when integrated with a automated test generation tool (EvoSuite), our approach finds 54 real world bugs in large-scale Java programs, including \DISTINCTBUGS{} bugs that are not found by any other automated testing method in our evaluation.

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