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Computer programs --- Computer software --- Testing --- Catalogs --- -Computer software --- -Computer program files --- Files, Computer program --- Program files, Computer --- Programs, Computer --- Computer files --- Software, Computer --- Computer systems --- -Testing --- Computer program testing --- Debugging in computer science --- Computer programs - Testing --- Computer software - Catalogs --- Computer programming tests
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Learn to write better automated tests that will dramatically increase your productivity and have fun while doing so. This book is a build-your-own adventure designed for individual reading and for collaborative workshops. You will build an xUnit automated test framework using JavaScript: initially a clone of Jest, but adding a couple of neat features borrowed from RSpec, the genre-defining tool for behavior-driven development (BDD). Along the way, you will explore the philosophy behind automated testing best practices. The automated test runner is one of the most important innovations within software engineering. But for many programmers, automated testing remains a mystery, and knowing how to write good tests is akin to sorcery. As the chapters of this book unfold, you will see how the humble test runner is an elegant and simple piece of software. Each chapter picks a single feature to build, like the "it" function or the "beforeEach" block. It picks apart the theory of why the feature needs to exist, and how to use it effectively in your own test suites. Every chapter ends with a set of ideas for extension points should you wish to explore further, alone or in groups. The book culminates in an implementation of test doubles and mocks—one of the most difficult and misunderstood concepts within automated testing. By the end of the book, you will have gained a solid understanding of automated testing principles that you can immediately apply to your work projects. You will: Build an xUnit automated test framework See how an automated test runner works Understand the best practices for automated unit testing Effectively use test doubles and mocks.
Computer programs—Testing. --- Software engineering. --- Programming languages (Electronic computers). --- Software Testing. --- Software Engineering. --- Programming Language. --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Electronic data processing --- Languages, Artificial --- Computer software engineering --- Engineering --- Computer software --- Testing. --- Software, Computer --- Computer systems --- Testing --- Design. --- Mechanical properties testing --- Reliability (Engineering) --- Grading --- Specifications --- Standardization
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Overhaul your debugging techniques and master the theory and tools needed to debug and troubleshoot cloud applications in production environments. This book teaches debugging skills that universities often avoid, but that typically consume as much as 60% of our time as developers. The book covers the use of debugger features such as tracepoints, object marking, watch renderers, and more. Author Shai Almog presents a scientific approach to debugging that is grounded in theory while being practical enough to help you to chase stubborn bugs through the maze of a Kubernetes deployment. Practical Debugging at Scale assumes a polyglot environment as is common for most enterprises, but focuses on JVM environments. Most of the tooling and techniques described are applicable to Python, Node, and other platforms, as well as to Java and other JVM languages. The book specifically covers debugging in production, an often-neglected discipline but an all too painful reality. You’ll learn modern techniques around observability, monitoring, logging, and full stack debugging that you can put to immediate use in troubleshooting common ailments in production environments. You Will Learn: The scientific method underlying the process of debugging Debugger capabilities such as tracepoints and marker objects The correct use of less understood features such as exception breakpoints Techniques for tracing issues in production Kubernetes environments Observability and monitoring to resolve production problems Industry best practices for common tooling such as logging Profiling to understand performance and memory problems .
Application software --- Debugging in computer science. --- Cloud computing. --- Development. --- Kubernetes. --- Electronic data processing --- Web services --- Computer programs --- Troubleshooting in computer science --- Data editing --- Software failures --- Development of application software --- Distributed processing --- Debugging --- Testing --- Java (Computer program language). --- Cloud Computing. --- Computer programming. --- Computer programs—Testing. --- Java. --- Programming Techniques. --- Software Testing. --- Computers --- Electronic computer programming --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Object-oriented programming languages --- JavaSpaces technology --- Programming
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Programming --- Computer programs --- Testing --- -681.3*D25 --- Computer program files --- Files, Computer program --- Program files, Computer --- Programs, Computer --- Computer files --- Computer software --- Testing and debugging: diagnostics; dumps; error handling and recovery; monitors; symbolic execution; test data generators; tracing (Software engineering) --- Computerprogramma's: testen --- Testing. --- Computerprogramma's: testen. --- 681.3*D25 Testing and debugging: diagnostics; dumps; error handling and recovery; monitors; symbolic execution; test data generators; tracing (Software engineering) --- Logiciels --- Essais --- 681.3*D25 --- Computer program testing --- Debugging in computer science --- Computer programs - Testing --- Tests
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Computer programs --- Programmes d'ordinateur --- Logiciels --- Reliability --- Congresses. --- Testing --- Verification --- Fiabilité --- Congrès --- Vérification --- Congresses --- -Computer programs --- -681.3*D2 --- Computer program files --- Files, Computer program --- Program files, Computer --- Programs, Computer --- Computer files --- Computer software --- -Congresses --- Software engineering: protection mechanisms; standards--See also {681.3*K63}; {681.3*K51} --- 681.3*D2 Software engineering: protection mechanisms; standards--See also {681.3*K63}; {681.3*K51} --- 681.3*D2 --- Reliability&delete& --- Testing&delete& --- Verification&delete& --- Computer programs - Reliability - Congresses --- Computer programs - Testing - Congresses --- Computer programs - Verification - Congresses
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This book constitutes the refereed proceedings of the 18th International Conference on Software Engineering and Formal Methods, SEFM 2020, held in Amsterdam, The Netherlands, in September 2020. The 16 full papers presented together with 1 keynote talk and an abstract of a keynote talk were carefully reviewed and selected from 58 submissions. The papers cover a large variety of topics, including testing, formal verification, program analysis, runtime verification, meta-programming and software development and evolution. The papers address a wide range of systems, such as IoT systems, human-robot interaction in healthcare scenarios, navigation of maritime autonomous systems, and operating systems. The Chapters "Multi-Purpose Syntax Definition with SDF3", “FRed: Conditional Model Checking via Reducers and Folders" and "Difference Verification with Conditions” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Software engineering. --- Artificial intelligence. --- Computer hardware. --- Special purpose computers. --- Software Engineering. --- Artificial Intelligence. --- Computer Hardware. --- Special Purpose and Application-Based Systems. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Computer software engineering --- Engineering --- Special purpose computers --- Computers --- Software engineering --- Artificial intelligence --- Formal methods (Computer science) --- Computer programs—Testing. --- Computers. --- Computers, Special purpose. --- Software Testing. --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace
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Computer programs --- Debugging in computer science --- Débogage --- Testing --- -Debugging in computer science --- 681.3*D24 --- 681.3*D25 --- Troubleshooting in computer science --- Data editing --- Electronic data processing --- Computer program files --- Files, Computer program --- Program files, Computer --- Programs, Computer --- Computer files --- Computer software --- Program verification: assertion checkers; correctness proofs; reliability; validation (Software engineering)--See also {681.3*F31} --- Testing and debugging: diagnostics; dumps; error handling and recovery; monitors; symbolic execution; test data generators; tracing (Software engineering) --- Debugging --- 681.3*D25 Testing and debugging: diagnostics; dumps; error handling and recovery; monitors; symbolic execution; test data generators; tracing (Software engineering) --- 681.3*D24 Program verification: assertion checkers; correctness proofs; reliability; validation (Software engineering)--See also {681.3*F31} --- Logiciels --- Essais --- Débogage --- Computer program testing --- Software failures --- Debugging in computer science. --- Computer programs - Testing
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