Best Practices for Unit Testing Methods That Use Cache Heavily?

Best practices for unit testing methods that use cache heavily?

Best Practices for Unit Testing Methods That Use Cache Heavily? 1

The Single Responsibility Principle is your best friend here.First of all, move AllFromCache() into a repository class and call it GetAll(). This makes testing your filtering class nice and easy. It no longer cares where you are getting it from.Second, wrap the class that gets the data from the database (or wherever) in a caching wrapper. AOP is a good technique for this. It's one of the few things that it's very good at.Using tools like PostSharp, you can set it up so that any method marked with a chosen attribute will be cached. However, if this is the only thing you are caching, you do not need to go as far as having an AOP framework. Just have a Repository and a Caching Wrapper that use the same interface and inject that into the calling how you've removed the repository implementation knowledge from the ProductManager? See also how you've adhered to Single Responsibility Principle by having a class that handles data extraction, a class that handles data retrieval and a class that handles caching?You can now instantiate the ProductManager with either of those Repositories and get caching . or not. (If you are using an IOC container, even better. It should be obvious how to adapt. The framework does things with it that are out of your control. Like, just removing stuff from it when it gets too full, for example. You are going to end up with tests that fail once in a blue moon and you will never really understand why. GetAll() just . gets all. From somewhere

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What is the difference between unit testing and debugging?

Testing is primarily performed by the testing team in order to find the defects in the system. Whereas, debugging is an activity performed by the development team. In debugging the cause of the defect is located and fixed. Thus removing the defect and preventing any future occurrence of the defect as well. Another difference between the two is - testing can be done without any internal knowledge of software architecture. Whereas debugging requires knowledge of software architecture and coding.To know more about career guidance regarding the Automation Testing field you can go through "ISTQB CTFL Performance Testing Training" posted by -"Narayanan Palani" on "udemy" website.

Best Practices for Unit Testing Methods That Use Cache Heavily? 2

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Is coding and unit testing violating the DRY principle

You are operating on a faulty premise.The code is not a description of the expected behavior, only the requirements and test cases are. And even then, the requirements and tests define two sides of the behavior. The requirements define the characteristics and functionality of the software system as a whole and test defines what the expected results should be and is matched against requirements. The code is a developer's interpretation of those requirements and the architecture of the system

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Is "Unit testing” a form of formal method?

The two are different things, and in fact much more different in practice than they would be in theory.A formal correctness proof proves something about the behaviour of an algorithm. For instance, it might investigate the invariants applying to the data as it is transformed by a sorting algorithm, and prove that if the algorithm terminates, every element is larger than the previous one. This kind of proof can be rigorous, i. e. if it's done correctly the algorithm cannot be wrong in this respect.In practice, algorithms must be embodied in computer code, and it's usually infeasible to prove that a given bit of code accurately represents the algorithm that you want. (That would require formally proving the behaviour of the compiler, the standard library, the virtual machine, etc. ) It gets a little easier the more similar the programing language is to the mathematical notation you've used in the formal proof, but not much. (The code running in central systems of the Space Shuttle was said to be almost a perfect mathematical notation itself, but not very pleasant to program in.)It's much more cost-effective to actually run the code on judiciously chosen inputs and verify that it produces the expected outputs. This has the disadvantage that you can never be certain that there is not an error in it - it might behave for those input/output pairs you have not tested (and there are usually more pairs than you can test, or you would not need a computer program to do the work in the first place), or worse, the code might be subtly non-deterministic or context-dependent in way that your tests do not expose.But in practice, most errors that affect a computation can be exposed with intelligent checks, and if you keep a record of known errors and test cases verifying they cannot recur, the quality of code can generally be made good enough to be of business value. Certainly it's a better idea to run unit tests, integration tests and user acceptance tests and get something out the door that people will pay for than to conduct a lengthy, expensive formal proof that overlooks a subtle deviation between the written specification and the actual expectations of the customer. So in the real world, the two are almost completely distinct activities

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