note-Effective Unit Testing by Eliotte Rusty Harold

本文深入探讨了单元测试的重要性和最佳实践,强调了每个测试只针对单一功能,避免条件逻辑,确保测试独立且可重复。文章提供了如何编写清晰、快速、无副作用的测试案例指导,以及如何避免测试的不确定性,如时间依赖、网络可用性等。

/**

  • https://www.youtube.com/watch?v=fr1E9aVnBxw
  • Verify that a know, fixed input produces a known, fixed output
  • If it’s a deterministic answer, write a characterization test.
  • If the problem is fuzzy or not perfectly defined, test a similar problem with less fuzzy answers.
  • Eliminate everything that makes input or output unclear or contingent.
  • Never generate random input. Always use fixed values.
  • Don’t use named constants from the model code. They may be wrong or change. Prefer literal strings and numbers.
  • Don’t access the network and preferably not the file system.
  • Control time, the speed of light, or the gravitational constant of the universe.
  • Write Your Tests First!
  • It’s not just about testing; it’s about software development
  • Test first development creates better API because you start with the user, not the used.
  • Test first hides implementation and avoids exposing internal implementation details. It avoids brittle, tightly coupled tests.
  • Why Unit Tests?
  • Unit measn One, Each test tests exactly one thing.
  • Each test method is one test
  • Best practice: one assert per test method
  • Share setup in a fixture, not the same method
  • You can have multiple test classes per model class. Do not feel compelled to stuff all your tests for Foo into FooTest.
  • Unit also means Independent
  • Tests can and do dun in any order
  • Tests can and do run in parallel in multiple threads.
  • Tests should not interfere with each other
  • Tests and Thread Safety
  • Don’t use synchronization, semaphores, or special data structures.
  • Do not share data between tests:
  • Do not use non-constant static fields in your tests.
  • Be wary of global state in the model code under test.
  • Best practice: one assert per test method.
  • Share setup in a fixture, not the same method.
  • The Two Least Known Facts of Unit Testing
    1. Tests do not share instance data.
    1. You can have many test classes per model class
  • Speed
  • A single test should run in a second or less.
  • A complete suite should run in a minute or less
  • Separate larger tests into additional suites
  • This is for ease of development.
  • Fail fast. Fun slowest tests last.
  • Passing tests should produce no output
  • Failing tests should produce clear output
  • Rotate your test data.
  • Don’t use the same data in every test.
  • E.G. don’ts set all ints you test to 3. Use 3,33,1117,-98,etc.
  • This makes it much easier to see immediately which test is failing and why.
  • Flakiness
  • Work really, really hard to avoid
  • Sources of flakiness:
  • Time dependence
  • Network availability
  • Explicit Randomness
  • Multithreading
  • System Skew
  • Undefined behaviour
  • Floating point roundoff
  • Integer width
  • Default character set
  • etc.
  • Avoid Conditional Logic in Tests
  • Debugging
  • Write a failing test before you fix the bug
  • If the test passes, the bug isn’t what you think it is.
  • Refactoring
  • Break the code before you refactor it
  • Do the tests fail?
  • Check your code coverage
  • If necessary, write additional tests before doing unsafe refactorings.
  • Development Practices
  • Use continuous integration(e.g. Travis).
  • Use a submit queue
  • Never, ever allow a check in with a failing test.
  • If it does happen, rollback first; ask questions later.
  • A red test blocks all merges. No further check ins until the build is green.
  • Final Thoughts
  • Write your test first.
  • Make all tests unambiguous and reproducible.
    */
基于实时迭代的数值鲁棒NMPC双模稳定预测模型(Matlab代码实现)内容概要:本文介绍了基于实时迭代的数值鲁棒非线性模型预测控制(NMPC)双模稳定预测模型的研究与Matlab代码实现,重点在于提升系统在存在不确定性与扰动情况下的控制性能与稳定性。该模型结合实时迭代优化机制,增强了传统NMPC的数值鲁棒性,并通过双模控制策略兼顾动态响应与稳态精度,适用于复杂非线性系统的预测控制问题。文中还列举了多个相关技术方向的应用案例,涵盖电力系统、路径规划、信号处理、机器学习等多个领域,展示了该方法的广泛适用性与工程价值。; 适合人群:具备一定控制理论基础和Matlab编程能力,从事自动化、电气工程、智能制造、机器人控制等领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于非线性系统的高性能预测控制设计,如电力系统调度、无人机控制、机器人轨迹跟踪等;②解决存在模型不确定性、外部扰动下的系统稳定控制问题;③通过Matlab仿真验证控制算法的有效性与鲁棒性,支撑科研论文复现与工程原型开发。; 阅读建议:建议读者结合提供的Matlab代码进行实践,重点关注NMPC的实时迭代机制与双模切换逻辑的设计细节,同时参考文中列举的相关研究方向拓展应用场景,强化对数值鲁棒性与系统稳定性之间平衡的理解。
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